面向目标跟踪的信息反馈融合方法研究
|
摘要
以信息融合理论为基础的目标跟踪技术指利用雷达、红外、声纳等多种传感器的观测信号对未知目标的数量、位置、速度、身份等状态进行估计的过程。目标跟踪技术已被广泛和成功地应用于军事和民用领域,包括国土防空、导航、制导、探测、定位、交通、制造、金融和医疗等。近年来,目标跟踪环境日趋复杂,强杂波干扰、大观测误差、低信噪比和高机动等大量不确定因素使传统目标跟踪技术受到严重挑战。由于传统目标跟踪技术采用单向开环融合模式,不利于信息的存储、挖掘与复用,导致复杂环境下跟踪效果下降。为此,本文将信息反馈融合的理念融入传统目标跟踪技术,较系统地研究了面向目标跟踪问题的信息反馈融合框架、方法和工程算法,主要研究成果如下：
1)针对传统信息融合方法缺乏信息反馈融合机制的缺陷,首创性地提出了空域信息融合平面与时域信息融合空间的概念,并构建了一般性的信息反馈融合框架。
2)针对传统变结构多模型方法缺乏后验信息反馈融合机制的缺陷,提出信息反馈融合最小熵变结构多模型方法(MEVSMM, Minimum Entropy Variable Structure Multiple-Model)和次优算法,将后验香农信息熵作为评价模型序列集的质量指标,利用信息反馈融合完成MSA(Model Sequence Set Adaptation)优化过程。与传统多模型方法相比,本方法估计精度更高、鲁棒性更好。
3)针对复杂观测误差环境下传统MSE(Minimum Shannon Entropy)方法跟踪效果退化的缺点,提出用GE(Geometrical Entropy)度量来补偿SE(Shannon Entropy)度量的估计偏差,并构建了信息反馈融合最小几何熵多模型方法(MGEMM, Minimum Geometrical Entropy Multiple-Model)和两个次优算法。与传统MSE方法相比,在先验观测误差分布与实际分布不一致时,本方法所得到的跟踪估计结果精度更高、鲁棒性更好。
4)针对传统多模型融合方法缺乏历史信息反馈处理机制的缺陷,提出历史信息反馈融合多模型估计方法(HFMM, Historical Feedback MM)和次优算法来解决历史估计信息的反馈融合问题。与传统多模型方法相比,本方法提升了历史融合信息的利用率,从而获得精度更高、鲁棒性更好的融合估计结果。
5)针对传统PHD(Probability Hypothesis Density)跟踪方法在缺乏目标初生先验信息时失效的缺陷,提出历史信息驱动反馈融合多目标跟踪方法HIFMTT (Historical Information Feedback Fusion Multiple Target Tracker)和次优算法。与传统PHD方法相比,无论是否具备目标初生先验信息,本方法都能较好地完成多目标跟踪工作。
6)针对传统HIFMTT方法在跟踪隐身目标时结果退化的缺陷,提出信息预测反馈融合多目标跟踪方法IPFMTT (Information Prediction Feedback Fusion Multiple Target Tracker)和次优算法来克服隐身目标观测检测率较低的困难。与传统HIFMTT方法相比,本方法以小幅增大跟踪虚警率的代价大幅提升了跟踪检测率。
The target tracking technology based on information fusion theory refers to the process of real-time estimating the states of the target quantity, position, velocity, identity, etc., based on the observations from the multiple sensors, such as radar, infrared, sonar, etc. The target tracking technology has been applied broadly and successfully to the military and civilian applications, including the national defense, navigation, guidance, detection, localization, transportation, manufacturing, finance, medicine, etc. The target tracking environments become more complex recently and the traditional target tracking technologies are challenged seriously by handling high level of uncertainties from environment, such as dense clutters, big observation errors, low SNR, high maneuvers, etc. Most traditional target tracking technologies adopt the single direction and open loop fusion modes and are weak in accumulating, mining and reusing the information. In consequence, the fusion performance of the traditional target tracking technologies will degenerate in complex environments. To this end, this thesis studies the information feedback fusion framework, methods and algorithms for the target tracking problems systematically, and the main results are as follows,
1) The traditional information fusion methods have the drawbacks of lacking of the information feedback mechanism. To this end, we first propose the concepts of the spacial information fusion plane and the temporal information fusing space, then construct a general information feedback fusion framework.
2) For the traditional variable structure multiple model methods have the drawbacks of lacking of the posterior information feedback mechanism, we propose the information feedback fusion minimum entropy variable structure multiple-model method (MEVSMM) and the sub-optimal algorithm. The proposed method takes the posterior Shannon Entropy as the optimization criterion for the model sequence set, and accomplishes the MSA process using the information feedback fusion mechanism. Compared to some traditional MM methods, the proposed method achieves more robust and accurate estimating results.
3) The traditional MSE based algorithms will degenerate in some complex observation error situations. To this end, we set up the GE measure to compensate the ill estimation problem deduced by the SE measure. Further, we propose the information feedback fusion minimum geometrical entropy multiple-model method (MGEMM) and two sub-optimal algorithms. Compared to some traditional MSE methods, the proposed method achieves more robust and accurate results when the prior observation error distribution is inconsistent with the real situation.
4) The traditional multiple-model fusion methods have the drawbacks of lacking of the mechanism of historical information feedback processing mechanism. To this end, we propose the historical feedback fusion multiple model method (HFMM) and the sub-optimal algorithm to realize the feedback fusing of the historical information. Compared to some traditional MM methods, the proposed method improves the information utilization ratio and achieves more robust and accurate estimating results.
5) For the traditional PHD tracker will lose its efficacy when there is little new target prior information, we propose the historical information feedback fusion multiple targets tracker (HIFMTT) and the sub-optimal algorithm. Compared to the traditional PHD methods, no matter the newborn target prior information is available or not, the proposed method can achieve desirable multiple-target tracking results
6) The traditional HIFMTT will deteriorate when tracking the stealth targets. To this end, we propose the information prediction feedback fusion multiple targets tracker (IPFMTT) and the sub-optimal algorithm to handle of low observation detection rate of the stealth target. Compared to the traditional HIFMTT methods, the proposed method improves the tracking detection rate significantly with only a slightly higher false alarm rate.
1)针对传统信息融合方法缺乏信息反馈融合机制的缺陷,首创性地提出了空域信息融合平面与时域信息融合空间的概念,并构建了一般性的信息反馈融合框架。
2)针对传统变结构多模型方法缺乏后验信息反馈融合机制的缺陷,提出信息反馈融合最小熵变结构多模型方法(MEVSMM, Minimum Entropy Variable Structure Multiple-Model)和次优算法,将后验香农信息熵作为评价模型序列集的质量指标,利用信息反馈融合完成MSA(Model Sequence Set Adaptation)优化过程。与传统多模型方法相比,本方法估计精度更高、鲁棒性更好。
3)针对复杂观测误差环境下传统MSE(Minimum Shannon Entropy)方法跟踪效果退化的缺点,提出用GE(Geometrical Entropy)度量来补偿SE(Shannon Entropy)度量的估计偏差,并构建了信息反馈融合最小几何熵多模型方法(MGEMM, Minimum Geometrical Entropy Multiple-Model)和两个次优算法。与传统MSE方法相比,在先验观测误差分布与实际分布不一致时,本方法所得到的跟踪估计结果精度更高、鲁棒性更好。
4)针对传统多模型融合方法缺乏历史信息反馈处理机制的缺陷,提出历史信息反馈融合多模型估计方法(HFMM, Historical Feedback MM)和次优算法来解决历史估计信息的反馈融合问题。与传统多模型方法相比,本方法提升了历史融合信息的利用率,从而获得精度更高、鲁棒性更好的融合估计结果。
5)针对传统PHD(Probability Hypothesis Density)跟踪方法在缺乏目标初生先验信息时失效的缺陷,提出历史信息驱动反馈融合多目标跟踪方法HIFMTT (Historical Information Feedback Fusion Multiple Target Tracker)和次优算法。与传统PHD方法相比,无论是否具备目标初生先验信息,本方法都能较好地完成多目标跟踪工作。
6)针对传统HIFMTT方法在跟踪隐身目标时结果退化的缺陷,提出信息预测反馈融合多目标跟踪方法IPFMTT (Information Prediction Feedback Fusion Multiple Target Tracker)和次优算法来克服隐身目标观测检测率较低的困难。与传统HIFMTT方法相比,本方法以小幅增大跟踪虚警率的代价大幅提升了跟踪检测率。
The target tracking technology based on information fusion theory refers to the process of real-time estimating the states of the target quantity, position, velocity, identity, etc., based on the observations from the multiple sensors, such as radar, infrared, sonar, etc. The target tracking technology has been applied broadly and successfully to the military and civilian applications, including the national defense, navigation, guidance, detection, localization, transportation, manufacturing, finance, medicine, etc. The target tracking environments become more complex recently and the traditional target tracking technologies are challenged seriously by handling high level of uncertainties from environment, such as dense clutters, big observation errors, low SNR, high maneuvers, etc. Most traditional target tracking technologies adopt the single direction and open loop fusion modes and are weak in accumulating, mining and reusing the information. In consequence, the fusion performance of the traditional target tracking technologies will degenerate in complex environments. To this end, this thesis studies the information feedback fusion framework, methods and algorithms for the target tracking problems systematically, and the main results are as follows,
1) The traditional information fusion methods have the drawbacks of lacking of the information feedback mechanism. To this end, we first propose the concepts of the spacial information fusion plane and the temporal information fusing space, then construct a general information feedback fusion framework.
2) For the traditional variable structure multiple model methods have the drawbacks of lacking of the posterior information feedback mechanism, we propose the information feedback fusion minimum entropy variable structure multiple-model method (MEVSMM) and the sub-optimal algorithm. The proposed method takes the posterior Shannon Entropy as the optimization criterion for the model sequence set, and accomplishes the MSA process using the information feedback fusion mechanism. Compared to some traditional MM methods, the proposed method achieves more robust and accurate estimating results.
3) The traditional MSE based algorithms will degenerate in some complex observation error situations. To this end, we set up the GE measure to compensate the ill estimation problem deduced by the SE measure. Further, we propose the information feedback fusion minimum geometrical entropy multiple-model method (MGEMM) and two sub-optimal algorithms. Compared to some traditional MSE methods, the proposed method achieves more robust and accurate results when the prior observation error distribution is inconsistent with the real situation.
4) The traditional multiple-model fusion methods have the drawbacks of lacking of the mechanism of historical information feedback processing mechanism. To this end, we propose the historical feedback fusion multiple model method (HFMM) and the sub-optimal algorithm to realize the feedback fusing of the historical information. Compared to some traditional MM methods, the proposed method improves the information utilization ratio and achieves more robust and accurate estimating results.
5) For the traditional PHD tracker will lose its efficacy when there is little new target prior information, we propose the historical information feedback fusion multiple targets tracker (HIFMTT) and the sub-optimal algorithm. Compared to the traditional PHD methods, no matter the newborn target prior information is available or not, the proposed method can achieve desirable multiple-target tracking results
6) The traditional HIFMTT will deteriorate when tracking the stealth targets. To this end, we propose the information prediction feedback fusion multiple targets tracker (IPFMTT) and the sub-optimal algorithm to handle of low observation detection rate of the stealth target. Compared to the traditional HIFMTT methods, the proposed method improves the tracking detection rate significantly with only a slightly higher false alarm rate.
引文
[1]M. E. Liggins, D. L. Hall, J. Llinas. Handbook of Multi-sensor Data Fusion: Theory and Practice (Second Edition) [M].CRC Press, Taylor & Francis Group. New York.2009.
[2]韩崇昭,朱洪艳,段战胜.多源信息融合[M].清华大学出版社.2006.
[3]Y. Bar-Shalom, X. Rong Li, T. Kirubarajan. Estimation with Applications to Tracking and Navigation [M]. John Wiley & Sons.2001.
[4]C. Y. Chong, D. Garren, T. P. Grayson. Ground Target Tracking-a Historical Perspective [C]. IEEE Aerospace Conference Proceedings.2000,433-448.
[5]刘钢,刘明,匡海鹏,修吉宏,翟林培.多目标跟踪方法综述[J].电光与控制.2004,3(11)：26-29.
[6]T. Stemler, K. judd. A Guide to Using Shadowing Filters for Forecasting and State Estimation [J]. Physica D.2009,238:1260-1273.
[7]扬威,付耀文,龙建乾,黎湘.基于有限集统计学理论的目标跟踪技术研究综述[J].电子学报.2012,40(7)：1440-1447.
[8]R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems [J]. Journal of Basic Engineering.1960,82:35-45.
[9]K. S. Kaawaase, F. Chi, J. Shuhong, Q B. Ji. A Review on Selected Target Tracking Algorithms [J]. Information Technology Journal.2011,10(4): 691-702.
[10]M. E. Lggins, C. Y. Chong, I. Kadar, V. Vannicola. Distributed Fusion Architectures and Algorithms for Target Tracking [C]. Proceedings of IEEE. 1997:95-107.
[11]D. Smith, S. Singh. Approaches to Multisensor Data Fusion in Target Tracking: A Survey [J]. IEEE Transactions on Knowledge and Data Engineering.2006, 18(12):1696-1710.
[12]R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems [J]. Journal of Basic Engineering.1960,82:35-45.
[13]D. B. Reid. An Non-Gaussian Filter for Tracking Targets Moving Over Terrain [C].12th Asilomar Conference on Circuits, Systems & Computers.1978: 112-116.
[14]G. Welch, G. Bishop. An Introduction to the Kalman Filter. Available on Line: http://clubs.ens-cachan.fr/krobot/old/data/positionnement/kalman.pdf.
[15]T. Benedict, G. Bordner. Synthesis of an Optimal Set of Radar Track-While-Scan Equations [J]. IEEE Transactions on Automatic Control. 1962,7:27-32.
[16]Y. Bar-Shalom. Tracking Methods in a Multitarget Environment [J]. IEEE Transactions on Automatic Control.1978,23(4):618-626.
[17]Y. Bar-shalom, T. E. Fortmann. Tracking in a Cluttered Environment with Probabilistic Data Association [J]. Automatica.1975,11:451-460.
[18]Y. Bar-shalom, T. E. Fortmann. Tracking and Data Association [M]. Academic Press.1988.
[19]I. J. Cox. A Review of Statistical Data Association Techniques for Motion Correspondence [J]. International Journal of Computer Vision.1993,10(1): 53-66.
[20]J. J. Leonard, H. F. Durrant-Whyte. Application of Multi-target Tracking to Sonar Based Mobile Robot Navigation [C].29th Conference of Decision and Control.1990:3118-3123.
[21]A. T. Alouani, T. R. Rice. On Optimal Asynchronous Track Fusion [C].1st IEEE Australian Symposium on Data Fusion.1996:147-152.
[22]徐毓,金以慧.多传感器异步关联航迹的融合[J].系统工程与电子技术2003,25(1)：1318-1320.
[23]S. S. Blackman, R. F. Popoli. Design and Analysis of Modern Tracking Systems [M].Artech House.1999.
[24]X. R. Li, V. P. Jilkov. Survey of Maneuvering Target Tracking Part I:Dynamic Models [J]. IEEE Transactions on Aerospace and Electronic Systems.2003, 39(4):1333-1364.
[25]B. Ristic, N. Gordon, A. Bessell. On Target Classification Using Kinematic Data [J]. Information Fusion.2004,5:15-21.
[26]P. Smets, B. Ristic. Kalman Filter and Joint Tracking and Classification Based on Belief Functions in the TBM Framework [J]. Information Fusion.2007, 8:16-27.
[27]T. Kurien, M. Liggins. Report-to-track Assignment in Multisensor Multitarget Tracking [C].27th IEEE Conference on Decision and Control.1988.
[28]赵宗贵,熊朝华,王珂,许阳.信息融合概念、方法与应用[M].国防工业出版社.2012.
[29]J. L. Speyer. Computation and Transmission Requirements for a Decentralized Linear-quadratic-Gaussian Control Problem [J]. IEEE Transactions on Automatical Control.1979,24:266-269.
[30]H. F. Durrant-Whyte, B. S. Y. Rao, H. Hu. Toward a Fullly Decentralized Architecture for Multi-sensor Data Fusion [C]. IEEE International Conference on Robot. Automat.1990.
[31]B. S. Y. Rao, H. F. Durrant-Whyte, J. A. Sheen. A Fully Decentralized Multi-sensor System for Tracking and Surveillance [J]. International Journal Robot. Research.1993,12(1):20-44.
[32]K. C. Chang. Evaluating Hierarchical Track Fusion with Information Matrix Filter [C]. In Proceeding of the Third International Society of Information Fusion.2000.
[33]K. C. Chang, S. mori, Z. Tian, C. Y. Chong. MAP Track Fusion Performance Evaluation [C]. In Proceeding of the Fifth International Society of Information Fusion.2002.
[34]A. O. Hero, D. Castanon, D. Cochran, K. Kastella. Foundations and Applications of Sensor Management [M]. Springer.2008.
[35]F. Gini, M. Rangaswamy. Knowledge-Based Radar Detection, Tracking, and Classification [M]. Wiley-Interscience Publication.2008.
[36]C. Kreucher, D. Blatt, A. Hero, K. Kastella. Adaptive Multi-modality Sensor Scheduling for Detection and Tracking of smart targets [J]. Digital Signal Processing.2005,16(5):546-567.
[37]C. M. Kreucher, A. O. Hero, K. D. kastella, M. R. Morelande. An Information-based Approach to Sensor Management in Large Dynamic Networks [C]. Proceedings of the IEEE.2007:978-999.
[38]胡卫东,郁文贤,卢建斌,王壮.相控阵雷达资源管理的理论与方法[M].国防工业出版社.2010.
[39]G. E. Monahan. A survey of Partially Observable Markov Decision Processes: Theory, Models and Algorithms[J]. Management Science.1982,28(1):1-16.
[40]Y. He, E. K. P. Chong. Sensor Scheduling for Target Tracking in Sensor Networks [C]. IEEE Conference on Decision and Control.2004:743-748
[41]Y. He, E. K. P. Chong. Sensor Scheduling for Target Tracking:A Monte Carlo Sampling Approach [J]. Digital Signal Processing.2006,16(5):533-545.
[42]C. M. Kreucher, K. Kastella, A. O. Hero. Information Based Sensor Management for Multitarget Tracking [C]. In SPIE Conference on Signal and Data Processing of Small Targets.2003:480-489.
[43]V. Krishnamurthy. Algorithms for Optimal Scheduling and Management of Hidden Markov Model Sensors [J]. IEEE Transactions on Signal Processing. 2002,50(6):1382-1397.
[44]J. Llinas, D. L. Hall. An Introduction to Multi-sensor Data fusion [C]. IEEE International Symposium on Circuits and Systems.1998:537-540.
[45]E. P. Blasch, S. Plano. JDL Level 5 Fusion Model "User Refinement" Issues and Applications in Group Tracking [C]. SPIE Processing on Signal Processing, Sensor Fusion, and Target Recognition.2002:270-279.
[46]A. N. Steinberg, C. L. Bowman, F. E. White. Revisions to the JDL Fusion Model [C]. SPIE Processing on Sensor Fusion:Architectures, Algorithms, and Applications III.1999:430-448.
[47]J. Llinas, C. Bowman, G. Rogova, A. Steinberg, E. Watlz, F. White. Revisiting the JDL Data Fusion Model II [C]. Available online: http://www.fusion2004.foi.se/papers/IF04-1218.pdf.
[48]F. Keinosuke. Introduction to Statistical Pattern Recognition [M]. Academic Press.1990.
[49]X. Chen, S. Jiao. The Image Stabilization Based on the Line Characters [C]. IEEE Proceedings of the World Congress on Computer Science and Information Engineering.2009:554-558.
[50]L. Scharf. Statistical Signal Processing Detection, Estimation and Time Series Analysis [M]. Addison-Wesley Publishing Company.1991.
[51]J. C. Bertein, R. Ceschi. Discrete Stochastic Processes and Optimal Filtering [M]. Wiley-ISTE.2010.
[52]E. W. Kamen, J K. Su. Introduction to Optimal Estimation [M]. Springer.1999.
[53]G. Welch, G. Bishop. An Introduction to the Kalman Filter.2001. Available online:http://132.206.230.229/e761/SIGGRAPH2001_CoursePack_08.pdf.
[54]Y. T. Chan, A. G. C. Hu, J. B. Plant. A Kalman Filter Based Tracking Scheme with input Estimation [J]. IEEE Transactions on Aerospace and Electronic Systems.1979,20(1):237-244.
[55]P. O. Gutman, M. Velger. Tracking Targets Using Adaptive Kalman Filtering [J]. IEEE Transactions on Aerospace and Electronic Systems.1990,26(5):691-699.
[56]A. Kiruluta, E. Eizenman, S. Pasupathy. Predictive Head Movement Tracking Using a Kalman Filter [J]. IEEE Transactions on Systems, Man, and Cybernetics.2002,27(2):326-331.
[57]S-T. Park, J. G. Lee. Improved Kalman Filter Design for Three Dimensional Radar Tracking [J]. IEEE Transactions on Aerospace and Electronic Systems. 2001,37(2):727-739.
[58]J. B. Pearson, E. B. Stear. Kalman Filter Applications in Airborne Radar Tracking [J]. IEEE Transactions on Aerospace and Electronic Systems.1974, 10(3):319-329.
[59]D. L. Alspach, H. W. Sorenson. Nonlinear Bayesian Estimation Using Gaussian Sum Approximations [J]. IEEE Transactions on Automatic Control.1972,17: 439-448.
[60]R. Karlsson. Simulation Based Methods for Target Tracking [D]. Ph.D. Thesis. Linkopings University, Linkoping, Sweden,2002.
[61]A. M. Sanjeev, S. Maskell, N. Gordon, T. Clapp. A Tutorial on Particle Filters for Online Nonlinear/non Gaussian Bayesian Tracking [J]. IEEE Transactions on Signal Processing.2002,50(2):174-188.
[62]N. Peach. Bearings-only Tracking Using a Set of Range-parameterized Extended Kalman Filter [J]. IEEE Proc. Control Theory and Application.1995, 142(1):73-80.
[63]S. Julier, J. Uhlmann. A New Extension of Kalman Filter to Nonlinear Systems [C]. The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls.1997:54-65.
[64]S. Julier. A Skewed Approach to Filtering [C]. The 12th International Symposium on Aerospace/Defense Sensing, Simulation and Controls.1998: 271-282.
[65]S. Julier, J. Uhlmann, H. Durrant-Whyte. A New Approach for Nonlinear Transformation of Means and Covariances in Filters and Estimators [J]. IEEE Transactions on Automatic Control.2000,45(3):477-482.
[66]B. Ristic, M Arulampalam. Tracking a Maneuvering Target Using Angle-Only Measurements:Algorithms and Performance [J]. Signal Process.2003,83(6): 1223-1238.
[67]石勇,韩崇昭.自适应UKF算法在目标跟踪中的应用[J].自动化学报.2011,37(6)：755-759.
[68]张树春,胡广大,刘思华.关于UKF方法的新探索及其在目标跟踪方面的应用[J].控制理论与应用.2006,23(4)：569-574.
[69]G. O. Roberts, J. S. Rosenthal. Markov Chain Monte Carlo:Some Practical Implications of Theoretical Results [J]. The Canadian Journal of Statistics.1998, 25(1):5-31.
[70]J. M. Hammersley, K. W. Morton. Poor Man's Monte Carlo [J]. Journal of Royal Statistical Society Series B.1954,16(1):23-38.
[71]N. J. Gordon, N. J. Salmond, A. F. M. Smith. Novel Approach to Non-Linear/Non-Gaussian Bayesian State Estimation [C]. IEEE Proceedings on Radar and Signal Processing F.1993,107-113.
[72]A. Doucet, J. F. G. de Freitas, N. J. Gordon. Sequential Monte Carlo Methods in practice [M]. New-York:Springer-Verlag.2001.
[73]A. Doucet. S. Godsill, C. Andrieu. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering [J]. Statistics and Computing.2000,10(3): 197-208.
[74]E. A. Wan, R. V. Merwe. The Unscented kalman Filter for Nonlinear Estimation [C].2000 Adaptive Systems for Signal processing, Communications, and Control Symposium.2000,153-158.
[75]E. A. Wan, R. V. Merwe. Kalman Filtering and Neural Networks [M]. Wilely. 2001.
[76]李景熹,王树宗.UPF算法及其在目标跟踪问题中的应用[J].系统仿真学报.2007,19(3)：675-678.
[77]D. Comaniciu, V. Ramesh, P, Meer. Kernel-based Object Tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,25(5): 564-577.
[78]C. Cheng, R. Ansari. Kernel Particle Filter for Visual Tracking [J]. IEEE Signal Processing letters.2005,12(3):242-245.
[79]J. Carpenter, P. Clifford, P. Fearnhead. Improved Particle Filter for Nonlinear Problems [J]. IEE Proceedings-Radar, Sonar and Navigation.1999,146(1):2-7.
[80]B. Ripley. Stochastic Simulation [M]. Wiley.1987.
[81]M. Pitt, N. Shephard. Filtering via Simulation:Auxiliary Particle Filters [J]. Journal of the American Statistical Association.1999,94(446):590-599.
[82]F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell. Particle Filters for Positioning, Navigation, and Tracking [J]. IEEE Transactions on Signal Processing.2002,50(2):425-437.
[83]X. Wang, J. F. Chen, Z. G. Shi, K. S. Chen. Fuzzy-Control-Based Particle Filter for Maneuvering Target Tracking [J]. PIER Progress in Electromagnetics Research.2011,118:1-15.
[84]P. Viktor, A. Saab, S. Egils. A Robust and Efficient Particle Filter for Target Tracking with Spatial Constraints [C].16th International Conference on Information Fusion.2013,30-37.
[85]J. Liu, P. Vadakkepat. Interacting MCMC Particle Filter for Tracking Maneuvering Target [J]. Digital Signal Processing.2010,20(2):561-574.
[86]孙伟,郭宝龙,朱娟娟,陈龙.一种新的层次粒子滤波的目标跟踪方法[J].光子学报.2010,39(5)：945-950.
[87]王书朋,姬红兵.用于目标跟踪的自适应粒子滤波算法[J].系统仿真学报.2010,22(3)：630-633.
[88]邱晓波,周启煌,窦丽华.机动目标跟踪的参数辨识模型[C].2009中国智能自动化会议.2009,558-563.
[89]L. Ljung. System Identification [M]. Prentice-Hall, Englewood Cliffs.1987.
[90]J. L. Anderson. An Ensemble Adjustment Kalman Filter For Data Assimilation [J]. Monthly Weather Review.2001,129(12):2884-2903.
[91]C. W. Hu, W. Chen, D J. Liu. Adaptive Kalman Filtering for Vehicle Navigation [J]. Journal of Global Positioning Systems.2003,2(1):42-47.
[92]M. Efe, J. A. Bather, D. P. Atherton. An Adaptive Kalman Filter with Sequential Rescaling of Process Noise [C].1999 American Control Conference.1999, 3913-3917.
[93]P. L. Bolger. Radar Principles with Applications to Tracking Systems [M]. Wiley. 1990.
[94]M. Munn. Comparison of Adaptive Target Tracking Algorithms for Phased-array Radar [J]. IEE Proceedings for Radar and Signal Processing.1992, 139(5):336-342.
[95]R. A. Singer. Estimating Optimal Tracking Filter Performance for Manned maneuvering Targets [J]. IEEE Transactions on Aerospace and Electronic Systems.1970,6(4):473-483.
[96]周宏仁,敬忠良,王培德.机动目标跟踪[M].国防工业出版社.1991.
[97]H. R. zhou, K S. Kumar. A "Current" Statistical Model and Adaptive Algorithm for Estimating Maneuvering Targets [J]. AIAA Journal of Guidance.1984,7(5): 596-602.
[98]Y. Bar-shalom, K. Birmiwal. Variable Dimension Filter for Maneuvering Target Tracking [J]. IEEE Transactions on Aerospace and Electronic Systems.1982, 18(5):611-619.
[99]S. K. Kashyap, G. Girija, j. R. Raol. Evaluation of Converted Measurement and Modified Extended Filters for Target Tracking [C]. AIAA Guidance, Navigation, and Control Conference and Exhibit.2003,1-11.
[100]D. T. Magill. Optimal Adaptive Estimation of Sampled Stochastic Processes [J]. IEEE Transactions on Automatic Control.1965,10(4):434-439.
[101]D. G. Lainiotis. Optimal Adaptive Estimation:Structure and Parameter Adaptation [J]. IEEE Transactions on Automatic Control.1971,16(2):160-170.
[102]Y. Bar-Shalom, W. D. Blair. Multitarget Multisensor Tracking:Applications and Advances [M]. Artech House.2000.
[103]H. A. Blom, Y. Bar-Shalom. The interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients [J]. IEEE Transactions on Automatic Control.1988,33(1):780-783.
[104]Y. Bar-Shalom, P. K. Willett, X. Tian. Tracking and Data Fusion [M]. CT:YBS. 2011.
[105]X. R. Li, Y. Bar-Shalom. Mode-set Adaptation in Multiple-model Estimators for Hybrid Systems [C]. In Proceedings of the 1992 American Control Conference.1992:1794-1799.
[106]N. Tudoroiu, K. Khorasani. Satellite Fault Diagnosis Using a Bank of Interacting Kalman Filters [J]. IEEE Transactions on Aerospace and Electronic Systems.2007,43(4):1334-1350.
[107]Y. Hayashi, H. Tsunashima, Y. Marumo. Detection of Railway Vehicles Using Multiple Model Approach [C]. International Joint Conference on SICE-ICASE. 2006:2812-2817.
[108]S. A. Gadsden, S. R. Habibi, T. A. Kirubarajan. Novel Interacting Multiple Model Method for Nonlinear Target Tracking [C].13th International Conference on Information Fusion.2010:1-8.
[109]邓小龙,谢剑英,杨煜普.基于交互式多模型的粒子滤波算法[J].系统仿真学报.2005,17(10)：2360-1366.
[110]彭冬亮,郭云飞,薛安克.三维高速机动目标跟踪交互式多模型算法[J].控制理论与应用.2008,25(5)：831-835.
[111]X. R. Li, Y. Bar-Shalom. Multiple-Model Estimation with Variable Structure [J]. IEEE Transactions on Automatic Control.1996,41(4):478-493.
[112]K. A. Fisher, P. S. Maybeck. Multiple-Model Adaptive Estimation with Filter Spawning [J]. IEEE Transactions on Aerospace and Electronic Systems.2002, 38(3):755-768.
[113]W. H. Wang, L. L. Li, D. H. Zhou. Robust State Estimation and Fault Diagnosis for Uncertain Hybrid Nonlinear Systems [J]. Nonlinear Analysis: Hybrid Systems.2007,1(1):2-15.
[114]M. karasalo, X. M. Hu. An Optimization Approach to Adaptive Kalman Filtering [J]. Automatica.2011,47(8):1785-1793.
[115]L. F. Xu, X. R. Li. Multiple Model Estimation by Hybrid Grid [C].2010 American Control Conference.2010,142-147.
[116]申屠晗,彭冬亮,薛安克.最小熵反馈式变结构多模型融合算法[J].控制理论与应用.2013,30(3)：372-378.
[117]王亚利,林家骏.基于Input Estimation的VSIMM机动目标跟踪[J].系统仿真学报.2009,21(3)：4143-4148.
[118]肖松,李志淮,谭贤四,王红.临近空间高超声速飞行器DG-VSMM跟踪算法[J].2013,25(2)：22-27.
[119]X. R. Li, X. R. Zhi, Y. M. Zhang. Multiple-Model Estimation with Variable Structure part III:Model-Group Switching Algorithm [J]. IEEE Transactions on Aerospace and Electronic Systems.1999,35(1):225-241.
[120]X. R. Li, Y. M. Zhang. Multiple-Model Estimation with Variable Structure Part V:Likely-Model Set Algorithm [J]. IEEE Transactions on Aerospace and Electronic Systems.2005,36(2):448-446.
[121]X. R. Li, V. P. Jilkov, J. F. Ru. Multiple-Model Estimation with Variable Structure Part VI:Expected-Mode Augmentation [J].2005,41(3):835-867.
[122]J. Lan, X. R. Li, C. D. Mu. Best Model Augmentation for Variable-Structure Multiple-Model Estimation [J]. IEEE Transactions on Aerospace and Electronic Systems.2011,47(3):2008-2025.
[123]J. Lan, X. R. Li. Equivalent-Model Augmentation for Variable-Structure Multiple-Model Estimation [C].14th International Conference on Information Fusion.2011:1-8.
[124]H. Kesavan, J. Kapur. Entropy Optimization Principles with Applications [M]. Academic Press.1992.
[125]S. Kullback, R. A.Leibler. On Information and Sufficiency [J]. The Annals of Mathematical Statistics 1951,22(1):79-86.
[126]R. W. Sitter. An Optimal Data Association Problem in Surveillance Theory [J]. IEEE Transactions on Military Electronics.1964,8(2):125-139.
[127]W. Blair, G. A. Watson, T. Kirubarajan, Y. Bar-Shalom. Benchmark for Radar Resource Allocation and Tracking in the Presence of ECM [J]. IEEE Transactions on Aerospace and Electronic Systems.1998,34(2):1015-1022.
[128]J. C. Ingemar. A Review of Statistical Data Association Techniques for Motion Correspondence [J]. International Journal of Computer Vision.1993,10(1): 53-66.
[129]G. W. Pulford. Relative Performance of Single-Scan Algorithms for Passive Sonar Tracking [C].2002 Innovation in Acoustics and Vibration Annual Conference of the Australian Acoustical Society.2002,212-221.
[130]Y. Bar-Shalom, E. Tse. Tracking in a Cluttered Environment with Probabilistic Data Association [J]. Automatica.1975,11(5):451-460.
[131]A. P. Henk, A. B. Edwin. Exact Bayesian Filter and Joint IMM Coupled PDA Tracking of Maneuvering Target from Possibly Missing and Flase Measurements [J]. Automatica.2006,42(1):127-135.
[132]黄扬帆,李正周,谭菊.改进PDA-AI方法的运动目标跟踪分析[J].重庆大学学报.2010,33(6)：77-82.
[133]陶然,张绪强.一种用于多目标跟踪的改进PDA算法[J].北京理工大学学报.2005,25(7)：621-624.
[134]Y. Bar-Shalom. Extension of the Probabilistic Data Association Filter to Multitarget Tracking [C].5th Symp. Nonlinear Estimation.1974,16-21.
[135]T. E. Fortmann, Y. Bar-Shalom, M Scheffe. Multi-Target Tracking Using Joint Probabilistic Data Association [C].19th IEEE Conference on Decision and Control Including the Symposium on Adaptive Processes.1980,807-812.
[136]B. Chen, J. K. Tugnait. Tracking of Multiple Maneuvering Target in Clutter Using IMM/JPDA Filtering and Fixed-Lag Smoothing [J]. Automatica.2001, 37(2):239-249.
[137]L. Svensson, D. Svensson, M. Guerriero, P. Willett. Set JPDA Filter for Multitarget Tracking [J]. IEEE Transactions on Signal Processing.2011,59(10): 4677-4691.
[138]T. E. Luginbuhl, E. H. Giannopoulos, P. L. Ainsleigh. A Modified JPDA [C]. 9th International Conference on Information Fusion.2006,1-8.
[139]K. C. Chang, C. Y. Chee, Y. Bar-Shalom. Joint Probabilistic Data Association in Distributed Sensor Networks [J]. IEEE Transactions on Automatic Control. 1986,31(10):889-897.
[140]万琴,王耀南,袁小芳.改进联合概率数据关联的视频多目标快速跟踪[J].控制理论与应用.2011,28(10)：1421-1430.
[141]许志刚,吴小俊.一种改进的分布式多目标跟踪算法[J].系统仿真学报.2012,24(5)：1026-1030.
[142]D. Reid. An Algorithm for Tracking Multiple Targets [J]. IEEE Transactions on Automatic Control.1979 24(6):423-432.
[143]S. Blackman. Multiple Hypothsis Tracking for Multple Target Tracking [J]. IEEE Transactions on Aerospace and Electronic Systems.2004,19(1):5-18.
[144]S. Blackman, R. Papoli. Design and Analysis of Modern Tracking Systems [M]. Artech House.1999.
[145]S. Coraluppi, C. Carthel. Multi-Hypothesis Sonar Tracking [C].7th Internationla Conference on Information Fusion.2004,33-40.
[146]T. Cham, J. Rehg. A Multiple Hypothesis Approach to Figure Tracking [C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.1999,239-246.
[147]T. Sathyan, S. Arulampalam, M. Mallick. Multiple Hypothesis Tracking with Multiframe Assignment Using Range and Rang-Rate Measurements [C].14th International Conference on Information Fusion.2011,5-8.
[148]P. Muthumanikandan, S. Vasuhi, V. Vaidehi. Multiple Maneuvering Target Tracking Using MHT and Nonliner Non-Gaussian Kalman Filter [C]. ICSCN International Conference on Signal Processing, Communications and Networking.2008,52-56.
[149]何佳洲,吴传利,周志华,陈世福.多假设跟踪技术综述[J].火力与指挥控制.2004,12(6)：1-5.
[150]张恒,樊晓平,瞿志华.基于多假设跟踪的移动机器人自适应蒙特卡罗定为研究[J].自动化学报.2009,33(9)：941-946.
[151]范炳艺,李建勋,刘坦.面向目标的概率多假设跟踪算法[J].航空学报.2012,31(12)：2373-2378.
[152]I. Goodman, R. Mahler, H. Nguyen. Mathematics of Data Fusion [M]. Kluwer Academic Publishers.1997.
[153]J. Goutsias, R. Mahler, H. Nguyen. Random Set Theory and Applications [M]. Springer-Verlag.1997.
[154]R. Mahler. Global Integrated Data Fusion [C].7th National Symposium on Sensor Fusion.1994,187-199.
[155]S. Mori, C. Chong, E. Tse, R. Wishner. Tracking and Identifying Multiple Targets without Apriori Identifications [J]. IEEE Transactions on Automatic Control.1986,21(1):401-409.
[156]R. Mahler. Multi-target Bayes Filtering Via First-Order Multi-Target Moments [J]. IEEE Transactions on Aerospace and Electronic Systems.2003,39(4): 1152-1178.
[157]B. Vo, S. Singh, A. Doucet. Sequential Monte Carlo Implementation of PHD Filter for Multi-Target Tracking [C]. Processing of the International Conference on Information Fusion.2003,792-799.
[158]B. Vo, S. Singh, A. Doucet. Sequential Monte Carlo Methods for Multi-Target Filtering with Random Finite Sets [J]. IEEE Transactions on Aerospace and Electronic Systems.2005,41(4):1224-1245.
[159]A. M. Johansen, S. Singh, A. Doucet, B. Vo. Convergence of the SMC Implementation of the PHD Filter [J]. Methodology and Computing in Applied Probability.2006,8(2):265-291.
[160]E. Clark, L. Bell. Convergence Results for the Particle PHD Filter [J]. IEEE Transactions on Signal Processing.2006,54(7):2652-2661.
[161]B. Vo, K. Ma. The Gaussian Mixture Probability Hypothesis Density Filter [J]. IEEE Transactions on Signal Processing.2006,54(11):4091-4104.
[162]E. Clark, B. Vo. Convergence Analysis of the Gaussian Mixture PHD Filter [J]. IEEE Transactions on Signal Processing,2007,55(4):1204-1212.
[163]E. Clark, B. T. Vo, B. N. Vo. Gaussian Particle Implementation of Probability Hypothesis Density Filters [C]. IEEE Aerospace Conference.2007,1-11.
[164]E. Clark, B. T. Vo. B. N. Vo, S. Godsill. Gaussian Mixture Implementations of Probability Hypothesis Density Filters for Non-Linear Dynamic Models [C]. IET Seminar on Target Tracking and Data Fusion.2008,21-28.
[165]R. Mahler. PHD Filters of Higher Order in Target Number [J]. IEEE Transactions on Aerospace and Electronic Systems.2009,45(4):1657-1664.
[166]B. T. Vo, B N. Vo, A. Cantoni. Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter [J]. IEEE Transactions on Signal Processing.2007,55(7):3553-3567.
[167]H. J. Zhang, Z. L. Jing, S. Q. Hu. Gaussian Mixture CPHD Filter with Gating Technique [J]. Signal Processing.2009,89(8):1521-1530.
[168]K. Panta, B. N. Vo, S. Singh, A. Doucet. Probability Hypothesis Density Filter Versus Multiple Hypothesis Tracking [C].8th Proceedings of SPIE Signal Processing, Sensor Fusion & Target Recognition.2004,284-295.
[169]K. Panta, B. N. Vo, S. Singh. Novel Data Association Schemes for the Probability Hypothesis Density Filter [J]. IEEE Transactions on Aerospace and Electronic Systems.2007,43(2):556-570.
[170]M. Tobias, A. Lanterman. Probability Hypothesis Density Based Multitarget Tracking with Multiple Biostatic Range and Doppler Observations [J]. IET Proceedings of Radar, Sonar and Navigation.2005,152(3):195-205.
[171]D. Clark, J. Bell. Bayesian Multiple Target Tracking in Forward Scan Sonar Images using the PHD Filter [J]. IET Proceedings of Radar, Sonar and Navigation.2005,152(5):327-334.
[172]D. Y. wang, J. K. Wu, A. A. Kassim, W. M. Huang. Data driven Probability Hypothesis Density Filter for Visual Tracking [J]. IEEE Transactions on Circuits and Systems for Video Technology.2008,18(8):1085-1095.
[173]J. Mullane, B. N. Vo, M. D. Adams, B. T. Vo. A Random Finite Set Approach to Bayesian SLAM [J]. IEEE Transactions on Robotics.2011,27(2):268-282.
[174]J. Mullane, B. N. Vo, M. D. Adams, B. T. Vo. Random Finite Sets for Robot Mapping and SLAM [M]. Springer-Verlag.2011.
[175]罗少华,徐晖,安玮,许丹,张志恒.粒子PHD滤波存活粒子采样新方法[J].国防科技大学学报.2012,34(2)：102-106.
[176]童慧思,张颢,孟东华,王希勒.PHD滤波在多目标检测前跟踪中的应用[J].电子学报.2011,39(9)：2046-2051.
[177]徐洋,徐晖,安玮,罗少华.一种利用信号幅度信息的光学传感器像平面GM-PHD滤波算法[J].宇航学报.2012,33(4)：478-486.
[178]张崇猛,王戈,孟凡彬,王夙歆,蔡玲.一种无迹粒子PHD滤波的多目标跟踪算法[J].系统仿真学报.2013,25(1)：94-98.
[179]Y. M. Zhu, Z. S. You, J Zhao. The Optimality for the Distributed Kalman Filtering Fusion with Feedback [J]. Automatica.2001,37(9):1489-1493.
[180]关欣,何友,衣晓.带反馈信息的纯方位水下分布式融合算法[J].系统仿真学报.2003,15(7)：947-949.
[181]S. L. Sun. Scalar Weighting Optimal and Adaptive Information Fusion Kalman Filter with Feedback [C]. Proceedings of the 5th Word Congress on Intelligent Control and Automation.2004:1628-1632.
[182]黄树峰,秦超英,郭培荣.带反馈信息的多传感器航迹融合算法[J].传感技术学报.2003,22(11)：1591-1595.
[183]李涛,王宝树,乔向东.带反馈多传感器分层融合算法性能研究[J].宇航学报.2005,26(6)：732-747.
[184]李辉程,张安,沈莹.基于反馈结构的多传感器自适应航迹融合算法[J].计算机学报.2006,19(12)：2232-2237.
[185]Y. Alain, A. Jose, D. Dona, M. Seron. Multisensor Fusion Fault Tolerant Control [J]. Automatica.2011,47(7):1461-1466.
[186]郭徽东,章新华,许林周,宋元.基于融合反馈的多传感器空间偏差配准方法[J].控制与决策.2007,22(5)：577-584.
[187]宋强,崔亚奇,何友.反馈式多目标多传感器系统误差融合技术[J].宇航学报.2011,32(1)：115-122.
[188]Y. Bar-Shalom. Multitarget-Multisensor Tracking:Advanced Applications [M]. Atech House.1990.
[189]付钿,牛玉刚.基于融合反馈式粒子滤波器的多目标跟踪算法[J].华东理工大学学报.2010,36(2)：261-266.
[190]Y. Wu, X. L. Luo. A Novel Calibration Approach of Soft Sensor Based on Multirate Data Fusion [J]. Journal of Process Control.2010,20(10):1252-1260.
[191]G. Battistelli, L. Chisici, S. Morrocchi. A Feedback Approach to Multitarget Multisensor Tracking with Application to Bearing-only Tracking [C].13th Conference on Information Fusion.2010:26-29.
[192]Z. Liang, W. Chaovalitwongse, A. Rodriguez, D. Jeffcoat. Optimization of Spatiotemporal Clustering for Target Tracking from Multisensor Data [J]. IEEE Transactions on Systems, Man and Cybernetics--Part C:Applications and Reviews.2010,40(2):176-187.
[193]J. George, J. Carssidis, T. Singh and A. Fosbury. Anomaly Detection Using Context-Aided Target Tracking [J]. Journal of Advances in Information Fusion. 2011,6(1):39-56.
[194]I. Reed, R. Gagliardi, L. Stotts. Optical Moving Target Detection with 3-D Matched Filtering [J]. IEEE Transactions on Aerospace and Electronic Systems. 1988,24(4):327-336.
[195]S. Buzzi, M. Lops and L. Venturino. Track-before-detect Procedures for Early Detection of Moving Target from Airborne Radars [J]. IEEE Transactions on Aerospace and Electronic Systems.2005,41(3):937-954.
[196]S. Buzzi, M. Lops and L. Venturino. Detection of an Unknown Number of Targets via Track-before-detect procedures [J]. IEEE Transactions on Aerospace and Electronic Systems.2008,44(3):1135-1149.
[197]A. O. Hero, D. Castanon, D. Cochran, K. Kastella. Foundations and Applications of Sensor Management [M]. Springer.2008.
[198]H. B. Mitchell. Data Fusion:Concepts and Ideas [M]. Springer.2012.
[199]N. Xiong, P. Svensson. Multisensor Management for Information Fusion: Issues and Approaches [J]. Information Fusion.2002,3(2):163-186.
[200]M. Zink, D. Westbrook, S. Abdallah, etc. Meteorological Command and Control [C]. In Procession of EESR 2005:Workshop on End-to-End, Sense-and-Respond Systems, Applications and Services.2005
[201]D. Hichings and D. A. Castanon. Receding Horizon Stochastic Control Algorithms for Sensor Management [C].2010 American Control Conference. 2010,6809-6815.
[202]C. Kreucher, K. Kastella, A. O. Hero. Sensor Management Using an Active Sensing Approach [J]. Signal Processing.2005,85(3):607-624.
[203]H. Wang, K. Yao, G. Pottie, D. Estrin. Entropy-based Sensor Selection Heuristic for Target Localization [C]. In Proc. IPSN 2004.2004:36-45.
[204]H. Shen-Tu, A. K. Xue, Y. F. Guo. Feedback Structure based Entropy Approach for Multiple-model Estimation [J]. Chinese Journal of Aeronautics. 2013,26(6):1506-1516.
[205]K. P. Nelson, B. J. Scannell, H. Landau. A Risk Profile for Information Fusion Algorithms [J]. Entropy.2011,13(8):1518-1532.
[206]M. G. Robert. Entropy and Information Theory [M]. Springer-Verlag. New York.2000.
[207]M. J. Hall. Universal Geometric Approach to Uncertainty, Entropy, and Information [J]. Physical Review A.1999,59(1):2602-2615.
[208]X. D. Wu, V. Kumar, J. R. Quinlan. The Top 10 Algorithms in Data Mining [M]. CRC Press, Boca Raton.2009.
[209]L. P. Jing, M. K. Ng, Z. X. Huang. An Entropy Weighting k-means algorithm for subspace clustering of high-dimensional sparse data [J]. IEEE Transactions on Knowledge and Data Engineering.2007,19(8):1026-1041.
[210]Y. Boers, J. N. Driessen. Interacting Multiple Model Particle Filter [C]. IEE Proceedings of Radar, Sonar and Navigation.2003:344-349.
[211]S. Mcginnity, G. W. Irwin. Multiple Model Bootstrap Filter for Maneuvering Target Tracking [J]. IEEE Transactions on Aerospace and Electronic Systems. 2000,36(3):1006-1012.
[212]王晓,韩崇朝.基于混合采样的多模型机动目标跟踪算法[J].自动化学报,2012,38(8)：1-5.
[213]黄鹤,王小旭,赵春晖,梁彦,潘泉.基于后验信息修正的自适应交互式多模型跟踪算法[J].西北工业大学学报.2011,29(6)：829-833.
[2]韩崇昭,朱洪艳,段战胜.多源信息融合[M].清华大学出版社.2006.
[3]Y. Bar-Shalom, X. Rong Li, T. Kirubarajan. Estimation with Applications to Tracking and Navigation [M]. John Wiley & Sons.2001.
[4]C. Y. Chong, D. Garren, T. P. Grayson. Ground Target Tracking-a Historical Perspective [C]. IEEE Aerospace Conference Proceedings.2000,433-448.
[5]刘钢,刘明,匡海鹏,修吉宏,翟林培.多目标跟踪方法综述[J].电光与控制.2004,3(11)：26-29.
[6]T. Stemler, K. judd. A Guide to Using Shadowing Filters for Forecasting and State Estimation [J]. Physica D.2009,238:1260-1273.
[7]扬威,付耀文,龙建乾,黎湘.基于有限集统计学理论的目标跟踪技术研究综述[J].电子学报.2012,40(7)：1440-1447.
[8]R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems [J]. Journal of Basic Engineering.1960,82:35-45.
[9]K. S. Kaawaase, F. Chi, J. Shuhong, Q B. Ji. A Review on Selected Target Tracking Algorithms [J]. Information Technology Journal.2011,10(4): 691-702.
[10]M. E. Lggins, C. Y. Chong, I. Kadar, V. Vannicola. Distributed Fusion Architectures and Algorithms for Target Tracking [C]. Proceedings of IEEE. 1997:95-107.
[11]D. Smith, S. Singh. Approaches to Multisensor Data Fusion in Target Tracking: A Survey [J]. IEEE Transactions on Knowledge and Data Engineering.2006, 18(12):1696-1710.
[12]R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems [J]. Journal of Basic Engineering.1960,82:35-45.
[13]D. B. Reid. An Non-Gaussian Filter for Tracking Targets Moving Over Terrain [C].12th Asilomar Conference on Circuits, Systems & Computers.1978: 112-116.
[14]G. Welch, G. Bishop. An Introduction to the Kalman Filter. Available on Line: http://clubs.ens-cachan.fr/krobot/old/data/positionnement/kalman.pdf.
[15]T. Benedict, G. Bordner. Synthesis of an Optimal Set of Radar Track-While-Scan Equations [J]. IEEE Transactions on Automatic Control. 1962,7:27-32.
[16]Y. Bar-Shalom. Tracking Methods in a Multitarget Environment [J]. IEEE Transactions on Automatic Control.1978,23(4):618-626.
[17]Y. Bar-shalom, T. E. Fortmann. Tracking in a Cluttered Environment with Probabilistic Data Association [J]. Automatica.1975,11:451-460.
[18]Y. Bar-shalom, T. E. Fortmann. Tracking and Data Association [M]. Academic Press.1988.
[19]I. J. Cox. A Review of Statistical Data Association Techniques for Motion Correspondence [J]. International Journal of Computer Vision.1993,10(1): 53-66.
[20]J. J. Leonard, H. F. Durrant-Whyte. Application of Multi-target Tracking to Sonar Based Mobile Robot Navigation [C].29th Conference of Decision and Control.1990:3118-3123.
[21]A. T. Alouani, T. R. Rice. On Optimal Asynchronous Track Fusion [C].1st IEEE Australian Symposium on Data Fusion.1996:147-152.
[22]徐毓,金以慧.多传感器异步关联航迹的融合[J].系统工程与电子技术2003,25(1)：1318-1320.
[23]S. S. Blackman, R. F. Popoli. Design and Analysis of Modern Tracking Systems [M].Artech House.1999.
[24]X. R. Li, V. P. Jilkov. Survey of Maneuvering Target Tracking Part I:Dynamic Models [J]. IEEE Transactions on Aerospace and Electronic Systems.2003, 39(4):1333-1364.
[25]B. Ristic, N. Gordon, A. Bessell. On Target Classification Using Kinematic Data [J]. Information Fusion.2004,5:15-21.
[26]P. Smets, B. Ristic. Kalman Filter and Joint Tracking and Classification Based on Belief Functions in the TBM Framework [J]. Information Fusion.2007, 8:16-27.
[27]T. Kurien, M. Liggins. Report-to-track Assignment in Multisensor Multitarget Tracking [C].27th IEEE Conference on Decision and Control.1988.
[28]赵宗贵,熊朝华,王珂,许阳.信息融合概念、方法与应用[M].国防工业出版社.2012.
[29]J. L. Speyer. Computation and Transmission Requirements for a Decentralized Linear-quadratic-Gaussian Control Problem [J]. IEEE Transactions on Automatical Control.1979,24:266-269.
[30]H. F. Durrant-Whyte, B. S. Y. Rao, H. Hu. Toward a Fullly Decentralized Architecture for Multi-sensor Data Fusion [C]. IEEE International Conference on Robot. Automat.1990.
[31]B. S. Y. Rao, H. F. Durrant-Whyte, J. A. Sheen. A Fully Decentralized Multi-sensor System for Tracking and Surveillance [J]. International Journal Robot. Research.1993,12(1):20-44.
[32]K. C. Chang. Evaluating Hierarchical Track Fusion with Information Matrix Filter [C]. In Proceeding of the Third International Society of Information Fusion.2000.
[33]K. C. Chang, S. mori, Z. Tian, C. Y. Chong. MAP Track Fusion Performance Evaluation [C]. In Proceeding of the Fifth International Society of Information Fusion.2002.
[34]A. O. Hero, D. Castanon, D. Cochran, K. Kastella. Foundations and Applications of Sensor Management [M]. Springer.2008.
[35]F. Gini, M. Rangaswamy. Knowledge-Based Radar Detection, Tracking, and Classification [M]. Wiley-Interscience Publication.2008.
[36]C. Kreucher, D. Blatt, A. Hero, K. Kastella. Adaptive Multi-modality Sensor Scheduling for Detection and Tracking of smart targets [J]. Digital Signal Processing.2005,16(5):546-567.
[37]C. M. Kreucher, A. O. Hero, K. D. kastella, M. R. Morelande. An Information-based Approach to Sensor Management in Large Dynamic Networks [C]. Proceedings of the IEEE.2007:978-999.
[38]胡卫东,郁文贤,卢建斌,王壮.相控阵雷达资源管理的理论与方法[M].国防工业出版社.2010.
[39]G. E. Monahan. A survey of Partially Observable Markov Decision Processes: Theory, Models and Algorithms[J]. Management Science.1982,28(1):1-16.
[40]Y. He, E. K. P. Chong. Sensor Scheduling for Target Tracking in Sensor Networks [C]. IEEE Conference on Decision and Control.2004:743-748
[41]Y. He, E. K. P. Chong. Sensor Scheduling for Target Tracking:A Monte Carlo Sampling Approach [J]. Digital Signal Processing.2006,16(5):533-545.
[42]C. M. Kreucher, K. Kastella, A. O. Hero. Information Based Sensor Management for Multitarget Tracking [C]. In SPIE Conference on Signal and Data Processing of Small Targets.2003:480-489.
[43]V. Krishnamurthy. Algorithms for Optimal Scheduling and Management of Hidden Markov Model Sensors [J]. IEEE Transactions on Signal Processing. 2002,50(6):1382-1397.
[44]J. Llinas, D. L. Hall. An Introduction to Multi-sensor Data fusion [C]. IEEE International Symposium on Circuits and Systems.1998:537-540.
[45]E. P. Blasch, S. Plano. JDL Level 5 Fusion Model "User Refinement" Issues and Applications in Group Tracking [C]. SPIE Processing on Signal Processing, Sensor Fusion, and Target Recognition.2002:270-279.
[46]A. N. Steinberg, C. L. Bowman, F. E. White. Revisions to the JDL Fusion Model [C]. SPIE Processing on Sensor Fusion:Architectures, Algorithms, and Applications III.1999:430-448.
[47]J. Llinas, C. Bowman, G. Rogova, A. Steinberg, E. Watlz, F. White. Revisiting the JDL Data Fusion Model II [C]. Available online: http://www.fusion2004.foi.se/papers/IF04-1218.pdf.
[48]F. Keinosuke. Introduction to Statistical Pattern Recognition [M]. Academic Press.1990.
[49]X. Chen, S. Jiao. The Image Stabilization Based on the Line Characters [C]. IEEE Proceedings of the World Congress on Computer Science and Information Engineering.2009:554-558.
[50]L. Scharf. Statistical Signal Processing Detection, Estimation and Time Series Analysis [M]. Addison-Wesley Publishing Company.1991.
[51]J. C. Bertein, R. Ceschi. Discrete Stochastic Processes and Optimal Filtering [M]. Wiley-ISTE.2010.
[52]E. W. Kamen, J K. Su. Introduction to Optimal Estimation [M]. Springer.1999.
[53]G. Welch, G. Bishop. An Introduction to the Kalman Filter.2001. Available online:http://132.206.230.229/e761/SIGGRAPH2001_CoursePack_08.pdf.
[54]Y. T. Chan, A. G. C. Hu, J. B. Plant. A Kalman Filter Based Tracking Scheme with input Estimation [J]. IEEE Transactions on Aerospace and Electronic Systems.1979,20(1):237-244.
[55]P. O. Gutman, M. Velger. Tracking Targets Using Adaptive Kalman Filtering [J]. IEEE Transactions on Aerospace and Electronic Systems.1990,26(5):691-699.
[56]A. Kiruluta, E. Eizenman, S. Pasupathy. Predictive Head Movement Tracking Using a Kalman Filter [J]. IEEE Transactions on Systems, Man, and Cybernetics.2002,27(2):326-331.
[57]S-T. Park, J. G. Lee. Improved Kalman Filter Design for Three Dimensional Radar Tracking [J]. IEEE Transactions on Aerospace and Electronic Systems. 2001,37(2):727-739.
[58]J. B. Pearson, E. B. Stear. Kalman Filter Applications in Airborne Radar Tracking [J]. IEEE Transactions on Aerospace and Electronic Systems.1974, 10(3):319-329.
[59]D. L. Alspach, H. W. Sorenson. Nonlinear Bayesian Estimation Using Gaussian Sum Approximations [J]. IEEE Transactions on Automatic Control.1972,17: 439-448.
[60]R. Karlsson. Simulation Based Methods for Target Tracking [D]. Ph.D. Thesis. Linkopings University, Linkoping, Sweden,2002.
[61]A. M. Sanjeev, S. Maskell, N. Gordon, T. Clapp. A Tutorial on Particle Filters for Online Nonlinear/non Gaussian Bayesian Tracking [J]. IEEE Transactions on Signal Processing.2002,50(2):174-188.
[62]N. Peach. Bearings-only Tracking Using a Set of Range-parameterized Extended Kalman Filter [J]. IEEE Proc. Control Theory and Application.1995, 142(1):73-80.
[63]S. Julier, J. Uhlmann. A New Extension of Kalman Filter to Nonlinear Systems [C]. The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls.1997:54-65.
[64]S. Julier. A Skewed Approach to Filtering [C]. The 12th International Symposium on Aerospace/Defense Sensing, Simulation and Controls.1998: 271-282.
[65]S. Julier, J. Uhlmann, H. Durrant-Whyte. A New Approach for Nonlinear Transformation of Means and Covariances in Filters and Estimators [J]. IEEE Transactions on Automatic Control.2000,45(3):477-482.
[66]B. Ristic, M Arulampalam. Tracking a Maneuvering Target Using Angle-Only Measurements:Algorithms and Performance [J]. Signal Process.2003,83(6): 1223-1238.
[67]石勇,韩崇昭.自适应UKF算法在目标跟踪中的应用[J].自动化学报.2011,37(6)：755-759.
[68]张树春,胡广大,刘思华.关于UKF方法的新探索及其在目标跟踪方面的应用[J].控制理论与应用.2006,23(4)：569-574.
[69]G. O. Roberts, J. S. Rosenthal. Markov Chain Monte Carlo:Some Practical Implications of Theoretical Results [J]. The Canadian Journal of Statistics.1998, 25(1):5-31.
[70]J. M. Hammersley, K. W. Morton. Poor Man's Monte Carlo [J]. Journal of Royal Statistical Society Series B.1954,16(1):23-38.
[71]N. J. Gordon, N. J. Salmond, A. F. M. Smith. Novel Approach to Non-Linear/Non-Gaussian Bayesian State Estimation [C]. IEEE Proceedings on Radar and Signal Processing F.1993,107-113.
[72]A. Doucet, J. F. G. de Freitas, N. J. Gordon. Sequential Monte Carlo Methods in practice [M]. New-York:Springer-Verlag.2001.
[73]A. Doucet. S. Godsill, C. Andrieu. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering [J]. Statistics and Computing.2000,10(3): 197-208.
[74]E. A. Wan, R. V. Merwe. The Unscented kalman Filter for Nonlinear Estimation [C].2000 Adaptive Systems for Signal processing, Communications, and Control Symposium.2000,153-158.
[75]E. A. Wan, R. V. Merwe. Kalman Filtering and Neural Networks [M]. Wilely. 2001.
[76]李景熹,王树宗.UPF算法及其在目标跟踪问题中的应用[J].系统仿真学报.2007,19(3)：675-678.
[77]D. Comaniciu, V. Ramesh, P, Meer. Kernel-based Object Tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,25(5): 564-577.
[78]C. Cheng, R. Ansari. Kernel Particle Filter for Visual Tracking [J]. IEEE Signal Processing letters.2005,12(3):242-245.
[79]J. Carpenter, P. Clifford, P. Fearnhead. Improved Particle Filter for Nonlinear Problems [J]. IEE Proceedings-Radar, Sonar and Navigation.1999,146(1):2-7.
[80]B. Ripley. Stochastic Simulation [M]. Wiley.1987.
[81]M. Pitt, N. Shephard. Filtering via Simulation:Auxiliary Particle Filters [J]. Journal of the American Statistical Association.1999,94(446):590-599.
[82]F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell. Particle Filters for Positioning, Navigation, and Tracking [J]. IEEE Transactions on Signal Processing.2002,50(2):425-437.
[83]X. Wang, J. F. Chen, Z. G. Shi, K. S. Chen. Fuzzy-Control-Based Particle Filter for Maneuvering Target Tracking [J]. PIER Progress in Electromagnetics Research.2011,118:1-15.
[84]P. Viktor, A. Saab, S. Egils. A Robust and Efficient Particle Filter for Target Tracking with Spatial Constraints [C].16th International Conference on Information Fusion.2013,30-37.
[85]J. Liu, P. Vadakkepat. Interacting MCMC Particle Filter for Tracking Maneuvering Target [J]. Digital Signal Processing.2010,20(2):561-574.
[86]孙伟,郭宝龙,朱娟娟,陈龙.一种新的层次粒子滤波的目标跟踪方法[J].光子学报.2010,39(5)：945-950.
[87]王书朋,姬红兵.用于目标跟踪的自适应粒子滤波算法[J].系统仿真学报.2010,22(3)：630-633.
[88]邱晓波,周启煌,窦丽华.机动目标跟踪的参数辨识模型[C].2009中国智能自动化会议.2009,558-563.
[89]L. Ljung. System Identification [M]. Prentice-Hall, Englewood Cliffs.1987.
[90]J. L. Anderson. An Ensemble Adjustment Kalman Filter For Data Assimilation [J]. Monthly Weather Review.2001,129(12):2884-2903.
[91]C. W. Hu, W. Chen, D J. Liu. Adaptive Kalman Filtering for Vehicle Navigation [J]. Journal of Global Positioning Systems.2003,2(1):42-47.
[92]M. Efe, J. A. Bather, D. P. Atherton. An Adaptive Kalman Filter with Sequential Rescaling of Process Noise [C].1999 American Control Conference.1999, 3913-3917.
[93]P. L. Bolger. Radar Principles with Applications to Tracking Systems [M]. Wiley. 1990.
[94]M. Munn. Comparison of Adaptive Target Tracking Algorithms for Phased-array Radar [J]. IEE Proceedings for Radar and Signal Processing.1992, 139(5):336-342.
[95]R. A. Singer. Estimating Optimal Tracking Filter Performance for Manned maneuvering Targets [J]. IEEE Transactions on Aerospace and Electronic Systems.1970,6(4):473-483.
[96]周宏仁,敬忠良,王培德.机动目标跟踪[M].国防工业出版社.1991.
[97]H. R. zhou, K S. Kumar. A "Current" Statistical Model and Adaptive Algorithm for Estimating Maneuvering Targets [J]. AIAA Journal of Guidance.1984,7(5): 596-602.
[98]Y. Bar-shalom, K. Birmiwal. Variable Dimension Filter for Maneuvering Target Tracking [J]. IEEE Transactions on Aerospace and Electronic Systems.1982, 18(5):611-619.
[99]S. K. Kashyap, G. Girija, j. R. Raol. Evaluation of Converted Measurement and Modified Extended Filters for Target Tracking [C]. AIAA Guidance, Navigation, and Control Conference and Exhibit.2003,1-11.
[100]D. T. Magill. Optimal Adaptive Estimation of Sampled Stochastic Processes [J]. IEEE Transactions on Automatic Control.1965,10(4):434-439.
[101]D. G. Lainiotis. Optimal Adaptive Estimation:Structure and Parameter Adaptation [J]. IEEE Transactions on Automatic Control.1971,16(2):160-170.
[102]Y. Bar-Shalom, W. D. Blair. Multitarget Multisensor Tracking:Applications and Advances [M]. Artech House.2000.
[103]H. A. Blom, Y. Bar-Shalom. The interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients [J]. IEEE Transactions on Automatic Control.1988,33(1):780-783.
[104]Y. Bar-Shalom, P. K. Willett, X. Tian. Tracking and Data Fusion [M]. CT:YBS. 2011.
[105]X. R. Li, Y. Bar-Shalom. Mode-set Adaptation in Multiple-model Estimators for Hybrid Systems [C]. In Proceedings of the 1992 American Control Conference.1992:1794-1799.
[106]N. Tudoroiu, K. Khorasani. Satellite Fault Diagnosis Using a Bank of Interacting Kalman Filters [J]. IEEE Transactions on Aerospace and Electronic Systems.2007,43(4):1334-1350.
[107]Y. Hayashi, H. Tsunashima, Y. Marumo. Detection of Railway Vehicles Using Multiple Model Approach [C]. International Joint Conference on SICE-ICASE. 2006:2812-2817.
[108]S. A. Gadsden, S. R. Habibi, T. A. Kirubarajan. Novel Interacting Multiple Model Method for Nonlinear Target Tracking [C].13th International Conference on Information Fusion.2010:1-8.
[109]邓小龙,谢剑英,杨煜普.基于交互式多模型的粒子滤波算法[J].系统仿真学报.2005,17(10)：2360-1366.
[110]彭冬亮,郭云飞,薛安克.三维高速机动目标跟踪交互式多模型算法[J].控制理论与应用.2008,25(5)：831-835.
[111]X. R. Li, Y. Bar-Shalom. Multiple-Model Estimation with Variable Structure [J]. IEEE Transactions on Automatic Control.1996,41(4):478-493.
[112]K. A. Fisher, P. S. Maybeck. Multiple-Model Adaptive Estimation with Filter Spawning [J]. IEEE Transactions on Aerospace and Electronic Systems.2002, 38(3):755-768.
[113]W. H. Wang, L. L. Li, D. H. Zhou. Robust State Estimation and Fault Diagnosis for Uncertain Hybrid Nonlinear Systems [J]. Nonlinear Analysis: Hybrid Systems.2007,1(1):2-15.
[114]M. karasalo, X. M. Hu. An Optimization Approach to Adaptive Kalman Filtering [J]. Automatica.2011,47(8):1785-1793.
[115]L. F. Xu, X. R. Li. Multiple Model Estimation by Hybrid Grid [C].2010 American Control Conference.2010,142-147.
[116]申屠晗,彭冬亮,薛安克.最小熵反馈式变结构多模型融合算法[J].控制理论与应用.2013,30(3)：372-378.
[117]王亚利,林家骏.基于Input Estimation的VSIMM机动目标跟踪[J].系统仿真学报.2009,21(3)：4143-4148.
[118]肖松,李志淮,谭贤四,王红.临近空间高超声速飞行器DG-VSMM跟踪算法[J].2013,25(2)：22-27.
[119]X. R. Li, X. R. Zhi, Y. M. Zhang. Multiple-Model Estimation with Variable Structure part III:Model-Group Switching Algorithm [J]. IEEE Transactions on Aerospace and Electronic Systems.1999,35(1):225-241.
[120]X. R. Li, Y. M. Zhang. Multiple-Model Estimation with Variable Structure Part V:Likely-Model Set Algorithm [J]. IEEE Transactions on Aerospace and Electronic Systems.2005,36(2):448-446.
[121]X. R. Li, V. P. Jilkov, J. F. Ru. Multiple-Model Estimation with Variable Structure Part VI:Expected-Mode Augmentation [J].2005,41(3):835-867.
[122]J. Lan, X. R. Li, C. D. Mu. Best Model Augmentation for Variable-Structure Multiple-Model Estimation [J]. IEEE Transactions on Aerospace and Electronic Systems.2011,47(3):2008-2025.
[123]J. Lan, X. R. Li. Equivalent-Model Augmentation for Variable-Structure Multiple-Model Estimation [C].14th International Conference on Information Fusion.2011:1-8.
[124]H. Kesavan, J. Kapur. Entropy Optimization Principles with Applications [M]. Academic Press.1992.
[125]S. Kullback, R. A.Leibler. On Information and Sufficiency [J]. The Annals of Mathematical Statistics 1951,22(1):79-86.
[126]R. W. Sitter. An Optimal Data Association Problem in Surveillance Theory [J]. IEEE Transactions on Military Electronics.1964,8(2):125-139.
[127]W. Blair, G. A. Watson, T. Kirubarajan, Y. Bar-Shalom. Benchmark for Radar Resource Allocation and Tracking in the Presence of ECM [J]. IEEE Transactions on Aerospace and Electronic Systems.1998,34(2):1015-1022.
[128]J. C. Ingemar. A Review of Statistical Data Association Techniques for Motion Correspondence [J]. International Journal of Computer Vision.1993,10(1): 53-66.
[129]G. W. Pulford. Relative Performance of Single-Scan Algorithms for Passive Sonar Tracking [C].2002 Innovation in Acoustics and Vibration Annual Conference of the Australian Acoustical Society.2002,212-221.
[130]Y. Bar-Shalom, E. Tse. Tracking in a Cluttered Environment with Probabilistic Data Association [J]. Automatica.1975,11(5):451-460.
[131]A. P. Henk, A. B. Edwin. Exact Bayesian Filter and Joint IMM Coupled PDA Tracking of Maneuvering Target from Possibly Missing and Flase Measurements [J]. Automatica.2006,42(1):127-135.
[132]黄扬帆,李正周,谭菊.改进PDA-AI方法的运动目标跟踪分析[J].重庆大学学报.2010,33(6)：77-82.
[133]陶然,张绪强.一种用于多目标跟踪的改进PDA算法[J].北京理工大学学报.2005,25(7)：621-624.
[134]Y. Bar-Shalom. Extension of the Probabilistic Data Association Filter to Multitarget Tracking [C].5th Symp. Nonlinear Estimation.1974,16-21.
[135]T. E. Fortmann, Y. Bar-Shalom, M Scheffe. Multi-Target Tracking Using Joint Probabilistic Data Association [C].19th IEEE Conference on Decision and Control Including the Symposium on Adaptive Processes.1980,807-812.
[136]B. Chen, J. K. Tugnait. Tracking of Multiple Maneuvering Target in Clutter Using IMM/JPDA Filtering and Fixed-Lag Smoothing [J]. Automatica.2001, 37(2):239-249.
[137]L. Svensson, D. Svensson, M. Guerriero, P. Willett. Set JPDA Filter for Multitarget Tracking [J]. IEEE Transactions on Signal Processing.2011,59(10): 4677-4691.
[138]T. E. Luginbuhl, E. H. Giannopoulos, P. L. Ainsleigh. A Modified JPDA [C]. 9th International Conference on Information Fusion.2006,1-8.
[139]K. C. Chang, C. Y. Chee, Y. Bar-Shalom. Joint Probabilistic Data Association in Distributed Sensor Networks [J]. IEEE Transactions on Automatic Control. 1986,31(10):889-897.
[140]万琴,王耀南,袁小芳.改进联合概率数据关联的视频多目标快速跟踪[J].控制理论与应用.2011,28(10)：1421-1430.
[141]许志刚,吴小俊.一种改进的分布式多目标跟踪算法[J].系统仿真学报.2012,24(5)：1026-1030.
[142]D. Reid. An Algorithm for Tracking Multiple Targets [J]. IEEE Transactions on Automatic Control.1979 24(6):423-432.
[143]S. Blackman. Multiple Hypothsis Tracking for Multple Target Tracking [J]. IEEE Transactions on Aerospace and Electronic Systems.2004,19(1):5-18.
[144]S. Blackman, R. Papoli. Design and Analysis of Modern Tracking Systems [M]. Artech House.1999.
[145]S. Coraluppi, C. Carthel. Multi-Hypothesis Sonar Tracking [C].7th Internationla Conference on Information Fusion.2004,33-40.
[146]T. Cham, J. Rehg. A Multiple Hypothesis Approach to Figure Tracking [C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.1999,239-246.
[147]T. Sathyan, S. Arulampalam, M. Mallick. Multiple Hypothesis Tracking with Multiframe Assignment Using Range and Rang-Rate Measurements [C].14th International Conference on Information Fusion.2011,5-8.
[148]P. Muthumanikandan, S. Vasuhi, V. Vaidehi. Multiple Maneuvering Target Tracking Using MHT and Nonliner Non-Gaussian Kalman Filter [C]. ICSCN International Conference on Signal Processing, Communications and Networking.2008,52-56.
[149]何佳洲,吴传利,周志华,陈世福.多假设跟踪技术综述[J].火力与指挥控制.2004,12(6)：1-5.
[150]张恒,樊晓平,瞿志华.基于多假设跟踪的移动机器人自适应蒙特卡罗定为研究[J].自动化学报.2009,33(9)：941-946.
[151]范炳艺,李建勋,刘坦.面向目标的概率多假设跟踪算法[J].航空学报.2012,31(12)：2373-2378.
[152]I. Goodman, R. Mahler, H. Nguyen. Mathematics of Data Fusion [M]. Kluwer Academic Publishers.1997.
[153]J. Goutsias, R. Mahler, H. Nguyen. Random Set Theory and Applications [M]. Springer-Verlag.1997.
[154]R. Mahler. Global Integrated Data Fusion [C].7th National Symposium on Sensor Fusion.1994,187-199.
[155]S. Mori, C. Chong, E. Tse, R. Wishner. Tracking and Identifying Multiple Targets without Apriori Identifications [J]. IEEE Transactions on Automatic Control.1986,21(1):401-409.
[156]R. Mahler. Multi-target Bayes Filtering Via First-Order Multi-Target Moments [J]. IEEE Transactions on Aerospace and Electronic Systems.2003,39(4): 1152-1178.
[157]B. Vo, S. Singh, A. Doucet. Sequential Monte Carlo Implementation of PHD Filter for Multi-Target Tracking [C]. Processing of the International Conference on Information Fusion.2003,792-799.
[158]B. Vo, S. Singh, A. Doucet. Sequential Monte Carlo Methods for Multi-Target Filtering with Random Finite Sets [J]. IEEE Transactions on Aerospace and Electronic Systems.2005,41(4):1224-1245.
[159]A. M. Johansen, S. Singh, A. Doucet, B. Vo. Convergence of the SMC Implementation of the PHD Filter [J]. Methodology and Computing in Applied Probability.2006,8(2):265-291.
[160]E. Clark, L. Bell. Convergence Results for the Particle PHD Filter [J]. IEEE Transactions on Signal Processing.2006,54(7):2652-2661.
[161]B. Vo, K. Ma. The Gaussian Mixture Probability Hypothesis Density Filter [J]. IEEE Transactions on Signal Processing.2006,54(11):4091-4104.
[162]E. Clark, B. Vo. Convergence Analysis of the Gaussian Mixture PHD Filter [J]. IEEE Transactions on Signal Processing,2007,55(4):1204-1212.
[163]E. Clark, B. T. Vo, B. N. Vo. Gaussian Particle Implementation of Probability Hypothesis Density Filters [C]. IEEE Aerospace Conference.2007,1-11.
[164]E. Clark, B. T. Vo. B. N. Vo, S. Godsill. Gaussian Mixture Implementations of Probability Hypothesis Density Filters for Non-Linear Dynamic Models [C]. IET Seminar on Target Tracking and Data Fusion.2008,21-28.
[165]R. Mahler. PHD Filters of Higher Order in Target Number [J]. IEEE Transactions on Aerospace and Electronic Systems.2009,45(4):1657-1664.
[166]B. T. Vo, B N. Vo, A. Cantoni. Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter [J]. IEEE Transactions on Signal Processing.2007,55(7):3553-3567.
[167]H. J. Zhang, Z. L. Jing, S. Q. Hu. Gaussian Mixture CPHD Filter with Gating Technique [J]. Signal Processing.2009,89(8):1521-1530.
[168]K. Panta, B. N. Vo, S. Singh, A. Doucet. Probability Hypothesis Density Filter Versus Multiple Hypothesis Tracking [C].8th Proceedings of SPIE Signal Processing, Sensor Fusion & Target Recognition.2004,284-295.
[169]K. Panta, B. N. Vo, S. Singh. Novel Data Association Schemes for the Probability Hypothesis Density Filter [J]. IEEE Transactions on Aerospace and Electronic Systems.2007,43(2):556-570.
[170]M. Tobias, A. Lanterman. Probability Hypothesis Density Based Multitarget Tracking with Multiple Biostatic Range and Doppler Observations [J]. IET Proceedings of Radar, Sonar and Navigation.2005,152(3):195-205.
[171]D. Clark, J. Bell. Bayesian Multiple Target Tracking in Forward Scan Sonar Images using the PHD Filter [J]. IET Proceedings of Radar, Sonar and Navigation.2005,152(5):327-334.
[172]D. Y. wang, J. K. Wu, A. A. Kassim, W. M. Huang. Data driven Probability Hypothesis Density Filter for Visual Tracking [J]. IEEE Transactions on Circuits and Systems for Video Technology.2008,18(8):1085-1095.
[173]J. Mullane, B. N. Vo, M. D. Adams, B. T. Vo. A Random Finite Set Approach to Bayesian SLAM [J]. IEEE Transactions on Robotics.2011,27(2):268-282.
[174]J. Mullane, B. N. Vo, M. D. Adams, B. T. Vo. Random Finite Sets for Robot Mapping and SLAM [M]. Springer-Verlag.2011.
[175]罗少华,徐晖,安玮,许丹,张志恒.粒子PHD滤波存活粒子采样新方法[J].国防科技大学学报.2012,34(2)：102-106.
[176]童慧思,张颢,孟东华,王希勒.PHD滤波在多目标检测前跟踪中的应用[J].电子学报.2011,39(9)：2046-2051.
[177]徐洋,徐晖,安玮,罗少华.一种利用信号幅度信息的光学传感器像平面GM-PHD滤波算法[J].宇航学报.2012,33(4)：478-486.
[178]张崇猛,王戈,孟凡彬,王夙歆,蔡玲.一种无迹粒子PHD滤波的多目标跟踪算法[J].系统仿真学报.2013,25(1)：94-98.
[179]Y. M. Zhu, Z. S. You, J Zhao. The Optimality for the Distributed Kalman Filtering Fusion with Feedback [J]. Automatica.2001,37(9):1489-1493.
[180]关欣,何友,衣晓.带反馈信息的纯方位水下分布式融合算法[J].系统仿真学报.2003,15(7)：947-949.
[181]S. L. Sun. Scalar Weighting Optimal and Adaptive Information Fusion Kalman Filter with Feedback [C]. Proceedings of the 5th Word Congress on Intelligent Control and Automation.2004:1628-1632.
[182]黄树峰,秦超英,郭培荣.带反馈信息的多传感器航迹融合算法[J].传感技术学报.2003,22(11)：1591-1595.
[183]李涛,王宝树,乔向东.带反馈多传感器分层融合算法性能研究[J].宇航学报.2005,26(6)：732-747.
[184]李辉程,张安,沈莹.基于反馈结构的多传感器自适应航迹融合算法[J].计算机学报.2006,19(12)：2232-2237.
[185]Y. Alain, A. Jose, D. Dona, M. Seron. Multisensor Fusion Fault Tolerant Control [J]. Automatica.2011,47(7):1461-1466.
[186]郭徽东,章新华,许林周,宋元.基于融合反馈的多传感器空间偏差配准方法[J].控制与决策.2007,22(5)：577-584.
[187]宋强,崔亚奇,何友.反馈式多目标多传感器系统误差融合技术[J].宇航学报.2011,32(1)：115-122.
[188]Y. Bar-Shalom. Multitarget-Multisensor Tracking:Advanced Applications [M]. Atech House.1990.
[189]付钿,牛玉刚.基于融合反馈式粒子滤波器的多目标跟踪算法[J].华东理工大学学报.2010,36(2)：261-266.
[190]Y. Wu, X. L. Luo. A Novel Calibration Approach of Soft Sensor Based on Multirate Data Fusion [J]. Journal of Process Control.2010,20(10):1252-1260.
[191]G. Battistelli, L. Chisici, S. Morrocchi. A Feedback Approach to Multitarget Multisensor Tracking with Application to Bearing-only Tracking [C].13th Conference on Information Fusion.2010:26-29.
[192]Z. Liang, W. Chaovalitwongse, A. Rodriguez, D. Jeffcoat. Optimization of Spatiotemporal Clustering for Target Tracking from Multisensor Data [J]. IEEE Transactions on Systems, Man and Cybernetics--Part C:Applications and Reviews.2010,40(2):176-187.
[193]J. George, J. Carssidis, T. Singh and A. Fosbury. Anomaly Detection Using Context-Aided Target Tracking [J]. Journal of Advances in Information Fusion. 2011,6(1):39-56.
[194]I. Reed, R. Gagliardi, L. Stotts. Optical Moving Target Detection with 3-D Matched Filtering [J]. IEEE Transactions on Aerospace and Electronic Systems. 1988,24(4):327-336.
[195]S. Buzzi, M. Lops and L. Venturino. Track-before-detect Procedures for Early Detection of Moving Target from Airborne Radars [J]. IEEE Transactions on Aerospace and Electronic Systems.2005,41(3):937-954.
[196]S. Buzzi, M. Lops and L. Venturino. Detection of an Unknown Number of Targets via Track-before-detect procedures [J]. IEEE Transactions on Aerospace and Electronic Systems.2008,44(3):1135-1149.
[197]A. O. Hero, D. Castanon, D. Cochran, K. Kastella. Foundations and Applications of Sensor Management [M]. Springer.2008.
[198]H. B. Mitchell. Data Fusion:Concepts and Ideas [M]. Springer.2012.
[199]N. Xiong, P. Svensson. Multisensor Management for Information Fusion: Issues and Approaches [J]. Information Fusion.2002,3(2):163-186.
[200]M. Zink, D. Westbrook, S. Abdallah, etc. Meteorological Command and Control [C]. In Procession of EESR 2005:Workshop on End-to-End, Sense-and-Respond Systems, Applications and Services.2005
[201]D. Hichings and D. A. Castanon. Receding Horizon Stochastic Control Algorithms for Sensor Management [C].2010 American Control Conference. 2010,6809-6815.
[202]C. Kreucher, K. Kastella, A. O. Hero. Sensor Management Using an Active Sensing Approach [J]. Signal Processing.2005,85(3):607-624.
[203]H. Wang, K. Yao, G. Pottie, D. Estrin. Entropy-based Sensor Selection Heuristic for Target Localization [C]. In Proc. IPSN 2004.2004:36-45.
[204]H. Shen-Tu, A. K. Xue, Y. F. Guo. Feedback Structure based Entropy Approach for Multiple-model Estimation [J]. Chinese Journal of Aeronautics. 2013,26(6):1506-1516.
[205]K. P. Nelson, B. J. Scannell, H. Landau. A Risk Profile for Information Fusion Algorithms [J]. Entropy.2011,13(8):1518-1532.
[206]M. G. Robert. Entropy and Information Theory [M]. Springer-Verlag. New York.2000.
[207]M. J. Hall. Universal Geometric Approach to Uncertainty, Entropy, and Information [J]. Physical Review A.1999,59(1):2602-2615.
[208]X. D. Wu, V. Kumar, J. R. Quinlan. The Top 10 Algorithms in Data Mining [M]. CRC Press, Boca Raton.2009.
[209]L. P. Jing, M. K. Ng, Z. X. Huang. An Entropy Weighting k-means algorithm for subspace clustering of high-dimensional sparse data [J]. IEEE Transactions on Knowledge and Data Engineering.2007,19(8):1026-1041.
[210]Y. Boers, J. N. Driessen. Interacting Multiple Model Particle Filter [C]. IEE Proceedings of Radar, Sonar and Navigation.2003:344-349.
[211]S. Mcginnity, G. W. Irwin. Multiple Model Bootstrap Filter for Maneuvering Target Tracking [J]. IEEE Transactions on Aerospace and Electronic Systems. 2000,36(3):1006-1012.
[212]王晓,韩崇朝.基于混合采样的多模型机动目标跟踪算法[J].自动化学报,2012,38(8)：1-5.
[213]黄鹤,王小旭,赵春晖,梁彦,潘泉.基于后验信息修正的自适应交互式多模型跟踪算法[J].西北工业大学学报.2011,29(6)：829-833.