多传感器状态融合估计理论与应用研究
|
摘要
随着传感器技术、通信技术的发展，各种面向复杂应用背景的多传感器系统的研究越来越受到人们的关注。多传感器系统中，信息表现形式的多样性、信息数量的巨大性、信息关系的复杂性以及要求信息处理的及时性，都大大超出了人脑的信息综合处理能力，多传感器信息融合理论应运而生。
状态融合估计是多传感器信息融合理论的一个非常重要的研究领域，主要研究如何利用多个传感器的信息更准确地估计系统状态，在跟踪系统及需要精确估计的其他领域应用十分广泛。
本文的研究内容为多传感器信息融合理论中的状态融合估计理论，主要针对精确估计的实际应用中，状态融合估计理论存在的一些问题提出了解决方法。
针对分布式次优融合方法迭代计算复杂、无法用于多于三个传感器的系统的现状，提出了消除相关估计方差的分布式算法。该算法保持了分布式系统在结构上与计算上的优点，仿真实验表明，算法的性能与传统分布式次优融合算法的估计性能相近。
丰富和发展了目前的最优分布式、集中式融合方法，将最优融合估计算法推广到更一般的非标准多传感器系统中。针对存在控制输入、过程噪声与观测噪声相关且为非零均值高斯白噪声的多传感器系统，推导了二级、三级融合算法。算法形式更具一般性，很容易推广到更高级的多传感器系统。
定义并提出了多传感器系统的方差性能函数，从理论上严格证明了其与融合估计方差的关系，结论用仿真实验得到了验证。
研究了不确定多传感器系统的两种不确定描述模型及相应的集中式鲁棒融合估计方法，并通过仿真详细地比较了两种融合方法的频域、时域性能。从理论上严格证明了将集中式鲁棒融合估计转化为相同估计性能的分布式融合估计算法的条件。
论文系统的研究了各个测量传感器相关的多传感器融合系统。对于测量噪声相关矩阵是确定的，且该矩阵可以通过相似变换变成对角阵的多传感器系统，给出了最优集中式、分布式融合估计。对于其他的系统，给出
浙江大学博士学位论文
了分解一合并的融合估计方法。仿真实验表明，针对测量噪声相关的多传
感器系统，分解一合并的融合估计方法可以得到比一般的参数不确定鲁棒
融合估计方法更好的估计结果，具有更强的鲁棒估计性能。
关于多传感器信息融合理论在工业过程中的应用实际研究，论文将状
态融合估计理论应用于精密纸机的成纸定量估计中。指出了使用多个传感
器可以提高状态估计的性能，在部分传感器出现故障时仍然可以保证具有
较好的估计效果。但如果多个传感器使用不当，出现了测量噪声相关的情
况，若不能使用正确的状态估计方法，也会使系统估计性能下降。
Due to the advent of the sensor technology and communication technique, multi-sensor systems have recently attracted considerable attention, especially, those with diversified complex using background. The emerging interest of research into multi-sensor information theory is viewed as timely since the multiformity, massiness, complex and real-time processing of information in multi-sensor system has gone beyond the human brain capacity of processing information.
State fusion estimation is an important study field in the information fusion theory, mainly dealing with how to estimate the system state exactly by multi-sensors. It is usually applied in tracking system and other exact estimating systems.
This dissertation considers state fusion estimation of multisensor information fusion theory. The main work of here is to solve the problems when fusion estimation theory is applied in practice.
In details, the major contributions of this thesis are as the following:
As ,we know, distributed suboptimal method need complex compute processing and can't be used in the system containing more than 3 sensors. The optimized algorithm is developed, which avoids computing correlated estimate covariance and has the advantages of the distributed structure. Meanwhile, the simulations show the developed algorithm has the similar performance as the classical distributed suboptimal fusion method.
The present optimal distributed and centralized fusion methods are enriched and expanded. Two-levels and three levels algorithms are discussed in the more general system, which has control input and in which the processing noise and measurement noise are correlated Gaussian white noise with nonzero mean. The discussed algorithms have more universal formats and are easily
generalized to the more-levels system.
The dissertation defines and develops the covariance performance
function and proves that it can decide the fusion estimate covariance. The simulations identify the conclusions.
The dissertation studies the fusion estimation of uncertainty multisensor system. Two uncertain models and corresponding centralized robust fusion estimate methods are discussed. The simulations compared different fusion methods detailedly in frequency and time fields. Moreover., it's proved that with the exact transforming condition, robust centralized estimate can be transformed to the distributed fusion method with the same fusion estimate performance.
The dissertation systemically studies the multisensor system with correlated measurement noise. When the measurement noise covariance is certain matrix that can be transformed to a diagonal matrix by matrix resemble transform, the dissertation develops optimal centralized and distributed fusion estimate. For the other systems, the decomposed-combined fusion estimation method is discussed. The simulations show that the developed algorithm can obtain a better performance than the general robust fusion estimation methods for the multiserisors system with correlated measurement noise.
As to the application of information fusion theory, the state fusion estimation methods are applied in the basis weight estimation of exact paper machine. The paper studies several practical situations and points out that the estimate results can develop if more sensors are used, even though when some of them fail. But if the measurement noises are correlated, the estimation methods must be chosed correctly, or else the estimation performance may decline.
状态融合估计是多传感器信息融合理论的一个非常重要的研究领域，主要研究如何利用多个传感器的信息更准确地估计系统状态，在跟踪系统及需要精确估计的其他领域应用十分广泛。
本文的研究内容为多传感器信息融合理论中的状态融合估计理论，主要针对精确估计的实际应用中，状态融合估计理论存在的一些问题提出了解决方法。
针对分布式次优融合方法迭代计算复杂、无法用于多于三个传感器的系统的现状，提出了消除相关估计方差的分布式算法。该算法保持了分布式系统在结构上与计算上的优点，仿真实验表明，算法的性能与传统分布式次优融合算法的估计性能相近。
丰富和发展了目前的最优分布式、集中式融合方法，将最优融合估计算法推广到更一般的非标准多传感器系统中。针对存在控制输入、过程噪声与观测噪声相关且为非零均值高斯白噪声的多传感器系统，推导了二级、三级融合算法。算法形式更具一般性，很容易推广到更高级的多传感器系统。
定义并提出了多传感器系统的方差性能函数，从理论上严格证明了其与融合估计方差的关系，结论用仿真实验得到了验证。
研究了不确定多传感器系统的两种不确定描述模型及相应的集中式鲁棒融合估计方法，并通过仿真详细地比较了两种融合方法的频域、时域性能。从理论上严格证明了将集中式鲁棒融合估计转化为相同估计性能的分布式融合估计算法的条件。
论文系统的研究了各个测量传感器相关的多传感器融合系统。对于测量噪声相关矩阵是确定的，且该矩阵可以通过相似变换变成对角阵的多传感器系统，给出了最优集中式、分布式融合估计。对于其他的系统，给出
浙江大学博士学位论文
了分解一合并的融合估计方法。仿真实验表明，针对测量噪声相关的多传
感器系统，分解一合并的融合估计方法可以得到比一般的参数不确定鲁棒
融合估计方法更好的估计结果，具有更强的鲁棒估计性能。
关于多传感器信息融合理论在工业过程中的应用实际研究，论文将状
态融合估计理论应用于精密纸机的成纸定量估计中。指出了使用多个传感
器可以提高状态估计的性能，在部分传感器出现故障时仍然可以保证具有
较好的估计效果。但如果多个传感器使用不当，出现了测量噪声相关的情
况，若不能使用正确的状态估计方法，也会使系统估计性能下降。
Due to the advent of the sensor technology and communication technique, multi-sensor systems have recently attracted considerable attention, especially, those with diversified complex using background. The emerging interest of research into multi-sensor information theory is viewed as timely since the multiformity, massiness, complex and real-time processing of information in multi-sensor system has gone beyond the human brain capacity of processing information.
State fusion estimation is an important study field in the information fusion theory, mainly dealing with how to estimate the system state exactly by multi-sensors. It is usually applied in tracking system and other exact estimating systems.
This dissertation considers state fusion estimation of multisensor information fusion theory. The main work of here is to solve the problems when fusion estimation theory is applied in practice.
In details, the major contributions of this thesis are as the following:
As ,we know, distributed suboptimal method need complex compute processing and can't be used in the system containing more than 3 sensors. The optimized algorithm is developed, which avoids computing correlated estimate covariance and has the advantages of the distributed structure. Meanwhile, the simulations show the developed algorithm has the similar performance as the classical distributed suboptimal fusion method.
The present optimal distributed and centralized fusion methods are enriched and expanded. Two-levels and three levels algorithms are discussed in the more general system, which has control input and in which the processing noise and measurement noise are correlated Gaussian white noise with nonzero mean. The discussed algorithms have more universal formats and are easily
generalized to the more-levels system.
The dissertation defines and develops the covariance performance
function and proves that it can decide the fusion estimate covariance. The simulations identify the conclusions.
The dissertation studies the fusion estimation of uncertainty multisensor system. Two uncertain models and corresponding centralized robust fusion estimate methods are discussed. The simulations compared different fusion methods detailedly in frequency and time fields. Moreover., it's proved that with the exact transforming condition, robust centralized estimate can be transformed to the distributed fusion method with the same fusion estimate performance.
The dissertation systemically studies the multisensor system with correlated measurement noise. When the measurement noise covariance is certain matrix that can be transformed to a diagonal matrix by matrix resemble transform, the dissertation develops optimal centralized and distributed fusion estimate. For the other systems, the decomposed-combined fusion estimation method is discussed. The simulations show that the developed algorithm can obtain a better performance than the general robust fusion estimation methods for the multiserisors system with correlated measurement noise.
As to the application of information fusion theory, the state fusion estimation methods are applied in the basis weight estimation of exact paper machine. The paper studies several practical situations and points out that the estimate results can develop if more sensors are used, even though when some of them fail. But if the measurement noises are correlated, the estimation methods must be chosed correctly, or else the estimation performance may decline.
引文
[1] J. Linas, E. Waltz. Multisensor Data Fusion. Artech House, Noewood, Massachusettes, 1990
[2] D. L. Hall., Mathematical Techniques in Multisensor Date Fusion. Artech House, Boston, London, 1992
[3] 赵忠贵等编译，多传感器融合，电子工业部二十八所，1993年2月，南京
[4] 赵忠贵编译，数据融合方法概论，电子工业部二十八所，1998年4月，南京
[5] 康耀红，，数据融合理论与应用，西安电子科技大学出版社，1997年11月，西安
[6] 何友，多目标多传感器综合算法研究，硕士论文，海军工程大学，1988年2月，武汉
[7] 王祁，聂伟，张兆礼，数据融合与智能传感器系统，传感器技术，1998，17(6)，51-53
[8] 何友，王国宏等，多传感器信息融合及其应用，电子工业山版社，2000
[9] L. Valet, G. Mauris, Ph. Bolon, A Statistical Overview of Recent Literature on Information Fusion, IEEE AESS Systems Magazine, March 2001, 7-14
[10] 黄瑛，多传感器数据融合系统的软件实现，传感器技术，17(6)，1998，9-11
[11] Eloi Bosse, Jean Roy, Stephane Paradis, Modeling and Simulation in Support of the Design of a Data Fusion System, Information Fusion, 2000(1), 27-87
[12] Anlan N. Steinberg, Data Fusion System Engineering, IEEE AESS Systems Magazine, June 2001,7-14
[13] 刘同明等，数据融合技术及其应用，国防工业出版社，1998
[14] 郭志恒，杨春英，机载多传感器两种数据融合方法探讨，电光与控制，67(3)，1997，14-19
[15] 戴筠，王建海，分布序贯最近邻多目标跟踪算法，系统工程与电子技术，1998，第7期，11-14
[16] 卢君明，李瑞棠，组网雷达自组织最小均方差数据融合算法，西安电子科技大学学报，27(2)，2000，161-165
[17] 张军英，多雷达站数据融合处理的聚类方法，计算机仿真，17(3)，2000，8-10，26
[18] Peter S. Maybeck, Robert L. Jensen, An Adaptive Extended Kalman Filter for Target Image Tracking, IEEE Transaction on Aerospace and Electronic System, Vol. AES-17, No.2, March, 1981, 173-179
[19] Yong-Jian Zheng, Bir Bhanu, Adaptive Object Detection from Multisensor Data, Proceedings of 1996 IEEE/SICE/RSI International Conference on Multisensor Fusion and Intelligent Systems, 633-640
[20] E. Jouseau, B. Dorizzi, Neural Network and Fuzzy Data Fusion. Application to an Online and Real Time Vehicle Detection System, Pattern Recognition Letters 20(1999), 97-107
[21] 刘源，谢维信，多传感器图像模糊融合算法在图像识别中的应用，西安电子科技大学学报27(1)，2000，5-8
[22] 宝音贺喜格，黄文虎，设备故障诊断的关联矩阵方法研究，振动与冲击，1999，18(1)，1-5
[23] 张雨，设备故障信息融合问题思考，长沙交通学院学报，1999，15(2)，22-29
[24] 刘燕燕等，数据融合技术在输电线网故障诊断中的应用，信息技术，2002，第8期，2-5
[25] 张彦泽，多传感器信息融合及在智能故障诊断中的应用，传感器技术，1999，18(2)，18-22
[26] 赵方，谢友柏，油液分析多技术集成的特征与信息融合，摩擦学学报，1998，18(1)，45-52
[27] 史天运等，柔性加工单元的状态监测与故障诊断研究，北京理工大学学报，1998，18(5)，567-572
[28] 董选明，裘丽华，基于BP算法的液压泵在线状态监测及故障诊断，北京航空航天大学学报，1997，22(2)，193-198
[29] Wadi, R. Balendra, An Intelligent Approach to Monitor and Control the Blanking Process, Advances in Engineering Software, 30(1999), 85-92
[30] P.G. Mathews, M. S. Shummugam, Neural-network Approach for Predicting hole quality in Reaming, International Journal of Machine Tools & Manufacture, 39(1999), 723-730
[31] Hrianmayee Vedam, Signed Digraph Based Multiple Fault diagnosis, Computer Chem. Engng. Vol. 27, 1997, 655-660
[32] Shang-liang Chen, Y. W. Jen, Data Fusion Neural Network for Tool Condition monitoring in CNC Milling Machining, International Journal of Machine Tools & Manufacture, 40(2000), 381-400
[33] 钢铁，吴林，超声检测中的多源信息融合技术与缺陷识别，1999，35(1)，11-14
[34] 庄钊文，郁文贤，王浩，信息融合技术在可靠性评估中的应用，系统工程与电子技术，2000，22(3)，75-77，80
[35] 郭呈贺等，多智能传感器的有限分散自治体系研究，机器人，1997，19(1)，28-34
[36] 罗志增，蒋静坪，基于DS理论的多信息融合方法及应用，电子学报，1999，27(9)，100-102
[37] 王祁，聂伟，基于信息融合技术的气体识别方法的研究，机器人，1999，21(4)，288-293
[38] 王祁，聂伟，张兆礼，数据融合与智能传感系统，传感器技术，17(6)，1998，51-53
[39] 王志武等，多传感器数据融合在切割机器人系统中的应用，上海交通大学学报，36(7)，2002，995-998
[40] Soo-Chang Pei and Lin-Gwo Lion, Vehicle-type Motion Estimation by the Fusion of Image Point and Line Features, Pattern Recognition, Vol.31, No.3, pp. 333-344, 1999
[41] L. Vergara, P. Bernabeu, Automatic Signal Detection Applied to Fire Control by Infrared Digital Signal Processing, Signal Processing, 80(2000), 659-669
[42] X. Dai, S. Khorram, Data Fusion Using Artificial Neural Network:
a Case Study on Multitemporal Cjange Analysis, Computer, Environment and Urban Systems, 23(1999), 19-31
[43] P. Arena, S. Baglio, Analog Cellular Networks for Multisenson Fusion and Control, IEEE Transaction on Circuits and Systems-Ⅰ: Fundamental Theory and Application, Vol. 47, No.9, September, 2000, 1378-1382
[44] 张九龙，模式识别的最大熵方法，2000，29(4)，152-156
[45] 艾克武，综合集成的内容与方法——复杂巨系统问题研究，系统工程与电子技术，1998，7，18-23
[46] Robert R. Tenney, Nils R. Sandell, Detection with Distributed Sensors, IEEE Transaction on Aerospace and Electronic System, Vol.AES, No.4, July, 1981, 501-510
[47] Z.Char, P.K. Varshney, Optimal Data Fusion in Multiple Sensor Detection System, IEEE Transaction on Aerospace and Electronic System, Vol. AES-22, No.1, January, 1986, 98-101
[48] A.R. Reilman, L.W. Nolte, Design and Performance Comparison of Distributed Detection Networks, IEEE Transaction on Aerospace and Electronic Systems, Vol. AES-23, No.6, November, 1987,789-797
[49] C.C. Lee, J. J. Chao, Optimaim Local Decision Space Partitioning for Distributed Detection, IEEE Transaction on Aerospace and Electronic Systems, Vol. 25, No.4, July, 1989, 538-544
[50] S.C.A. Thomopoulos, R.Viswanathan, D.K. Bougoulias, Optimal Distributed Detection Fusion, IEEE Transaction on Aerospace and Electronic Systems, Vol. 25, No.5, September, 1989, 761-765
[51] Moirad Barkat, Pramod K. Varshney, Decentralized CFAR Signal Detection, IEEE Transaction on Aerospace and Electronic Systems,Vol. 25, No.2, March, 1989, 141-149
[52] Guan Jian, He You, Peng Ying-Ning, Distributed CFAR Detector Based on Local Test Statistic, Signal Processing, 80(2000), 373-379
[53] Hermann Rohling, Radar CFAR Thresholding in Clutter and Multiple Target Situations, IEEE Transaction on Aerospace and
Electronic System, Vol. AES-19, No.4, July, 1983, 608-620
[54] Y. Bar-Shalom, H. M. Shertukde, K. R. Pattipati, Use of Measurements from an Imaging sensor for Precision Target Tracking,IEEE Transaction on Aerospace and Electronic Systems, Vol. 25,No.6, November, 1989, 863-871
[55] Evaggelos Geraniotis and Yawgeng A. Chau Distributed Detection of Weak Signals from Multiple Sensors with Correlated Observations, Proceedings of 27th Conference on Decision and Comrol, 1988, 2501-2506
[56] Yawgeng A. Chau and Evaggelos Geraniotis, Asymptotically Optimal Quantization and Fusion in Multiple Sensor Systems,Proceedings of 28th Conference on Decision and Control, 1989,585-587
[57] Yawgeng A. Chau and Evaggelos Geraniotis, Multi-sensor Correlation and Quantization in Distributed Detection Systems,Proceedings of 29th Conference on Decision and Control, 1990,2692-2097
[58] Wet Chang, Mosre Kam, Asynchronous Distributed Detection,IEEE Transaction on Aerospace and Electronic Systems, Vol. 30,No.3, July, 1994, 818-826
[59] Evaggelos Geranitis, Yawgeng A. Chau, Robust Data Fusion for Multisensor Detection Systems, IEEE Transaction on Information Theory, Vol. 36, No. 6, November, 1990, 1265-179
[60] Venugopal V. Veeravalli, Tamer Baser, Minimax Robust Decentralized Detection, IEEE Transaction on Information Theory,Vol. 40. No. 1. January. 1994. 35-40
[61] 苏慧敏等，多传感器数据融合的鲁棒检测技术，北京航空航天大学学报，1999，25(2)，156-159
[62] Allen J. Kanyuck, Robert A. Singer, Correlation of Multiple-Site Track Data, IEEE Transaction on Aerospace and Electronic System,Vol. AES-6, No.2, March, 1970, 180-187
[63] J.J. Stein, S.S. Blackman, Generalized Correlation of Multi-Target
Track Data, IEEE Transaction on Aerospace and Electronic System,Vol. AES-11, No.6, November, 1975, 1207-1217
[64] S.S. Blackman, Multiple-Target Target with Radar Application,Artech House, Norwood, MA, 1986
[65] Y. Bar-Shalom, T. Fortman, Tracking and Data Association,Academic Press, New York, 1998
[66] G.Y. Trunk, J.D. Wilson, Association of DF Bearing Measurement with Radar Tracks, IEEE Transaction on Aerospace and Electronic Systems, Vol. AES-23, No.4, July, 1987, 438-447
[67] Zhou, N. K. Bose, Multitarget Tracking in Clutter: Fast Algorithms for Data Assocition, IEEE Transaction on Aerospace and Electronic Systems, Vol. 29, No.2, April, 1993, 352-363
[68] J.A. Roecker, A Class of Near Optimal JPDA Algorithms, IEEE Transaction on Aerospace and Electronic Systems, Vol. 30, No.2,April, 1994, 504-510
[69] Fitzgerald R J. Development of Practical PDA Logic for Multitarget Tracking by Microprocessor, Proceedings of the American Controls Conference, Seattle, WA, June 1986, 889-893
[70] S. Mori, C. Harris, E. Rogers, Utilizing Fuzzy Models in the Design of Estimators and Predictors: an Agile Target Tracking Example, in Proceedings if the 2nd IEEE Conference on Fuzzy Systems, 1993, 679-684
[71] Y. Bar-Shalom, Multitarget Multisensor Tracking: Applications and Adveances, Vol. Ⅱ, Artech House, Norwood, MA, 1992
[72] Sengupta, R. Illtis, Neural Solution to the Multiple Target Tracking Data Association Problem, IEEE Trans. Aerospace Electron.System AES-25. 1989. 96-108
[73] 杨新星，焦李成，一种快速全局优化的神经网络及其在数据融合中的应用，电子科学学刊，1999，21(6)，820-824
[74] R.P. Singh, W. H. Bailey, Fuzzy Logic Applications to Multisensor-multitarget Correlation, IEEE Trans. Aerospace Electron.System AES-25, 1997, 752-769
[75] Y.M. Chen, H.C. Huang, Fuzzy Logic Approach to Mulrisensor Data Association, Mathematics and Computer in Simulation,52(2000), 399-412
[76] Ashraf M. Aziz, Murali Tummala, Roberto Cristi, Fuzzy Logic Data Correlation Approach in Multisensor-multitarget Tracking Systems, Signal Processing, 76(1999), 195-209
[77] 刘源，谢维信，基于多传感器多目标特征信息的模糊数据关联算法，系统工程与电子技术，1998，第12期，18-23
[78] 王明辉等，强干扰环境下性能优化的相互作用多模型-概率数据互联算法，自动化学报，27(2)，2001，267-270
[79] Singer R. and A. J. Kanyuck. Computer control of multiple site track correlation, Automation, Vol. 7, pp. 455-464, 1971
[80] D. Willner, C.B. Chang, K. P. Duunn, Kahman Filter Algorithm for a Multisensor System. In Proc IEEE Conf. Decision and Control.Dec. 1976
[81] M.F. Hassan, et al., A Decentralized Computational algorithm for the Global Kalman Filler. IEEE Trans. On Automat. Contr., Vo.,AC-23, 1978, pp262-268
[82] C.Y. Chong, Hierarchical Estimation. MIT/ONR C3 Workshop Monterey, CA, 1979
[83] Bar-shalom, Y., "On the track-to-track correlation problem", IEEE Transaction on Automatic Control, Vol. 26, pp. 571-572, 1981
[84] Bar-shalom, Y., Capmo, L., "The effect of the common process noise on the two-sensor fused-track covariance", IEEE Transaction on Aerospace and Electronic Systems, Vol. 22, pp. 803-805, 1986
[85] T. Kerr. Decentralized Filtering and Redundancy Management for Multisensor Navigation. IEEE Trans. On AES, Vol. 23, No. 1, 1987
[86] 何友，多目标多传感器综合算法，湖北省武汉地区研究生第三届学术年会，1987年第一期，1-11
[87] 何友，多传感器综合系统中的数据合成，全国C~3I年会论文，1998年11月，郑州
[88] 何友，多传感器网络中的分布估计，海军航空工程学院学报，
1988年第4期，1-8
[89] 周叶，戴冠中，王立新，线性离散时间系统分散估计的合成算法，控制与决策，1989年第1期，1-6
[90] 何友，多传感器系统中的航迹合成，火力与指挥控制，1990年第1期，7-14
[91] P.L. Liu, Local Estimation Combination for the Solving Tracking Problem. 1983 Asilomar Conf. On Circuits Systems and Computer,pp. 378-382
[92] A.S. Willsky, M.G. Bello, et al. Combining and Updating of Local Estimation and Regional Maps along Sets of One-dimensional Track. IEEE Trans. On AC, Vol. 27, No. 4, 1982, pp: 799-813
[93] Bierman B. J., Belzer M. R., A Decentrlized Square Root Information Filter/Smoother. Proceeding of 24th CDC, 1985,pp:1902-1905
[94] Neal A. Carlson, Federated Square Root Filter for Decentralized Parallel Processes, IEEE Trans. on Aerospace and Electronic Systems,vol. 26, pp. 517-525, May 1990.
[95] D.A. Castanon, et al. Nonlinear Data Fusion. In Proc. 1982 MIT/ONR Workshop Command Control, Montory, CA Aug. 1982
[96] D. A. Castanon and D. Tenekzis. Distributed Estimation Algorithm for Nonlinear Systems. IEEE Trans. on AC, Vol. 30, No.5,1985
[97] R. Lobbia, M. Kent. Data Fusion of Decentralized Local tracker Outputs. IEEE Trans. AES, Vol. 30, No.3, 1994:787-799
[98] Hamd R.Hashemipour, Summit Roy, and Alan J. Laub,"Decentralized Structure for Parallel Kalman Filtering", IEEE Transaction on Automatic Control, Vol.33, pp. 88-93, 1988
[99] K.C., Chang, Saha, R. K., Bar-Shalom, Y., "On optimal track-to-track fusion", IEEE Transaction on Aerospace and Electronic Systems, Vol. 33, pp. 1271-1276, 1997.
[100] T.H. Kerr. Comments on: Federated Square Root Filter for Decentralized Parallel Processes. IEEE Trans. on AES. Vol. 27, No. 6,
1991, pp: 946-948
[101] S. Roy, R. H. Hashem and A. J. Laub, Square Root Filter for Parallel Kalman Filtering Using Reduced-order Local Filters. IEEE trans, on AES, Vol. 27, No. 2, 1991, pp.276-288
[102] T. M. Berg, et al. General Decentralized Kalman Filters.Proceedings of the American Control Conference, Mayland, June 1994, pp: 2273-2274
[103] U.B. Desi and B. Das. Parallel algorithm for Kalman Filtering. In Proc. 1985 Amer. Contr. Conf., Boston, MA, June, 1985, pp: 920-921
[104] G. G. Meyer and H. W. Weinert. Parellel Algorithm and Computeational Structures for Linear Estimation Problems. In Statiatical Signal Processing, E. J. Wegman, New York, 1984, pp:507-516
[105] L. Hong Centealized and Distributed multisensor Integration with Uncertainties in Communication Networks. IEEE Trans. on AES, Vol.27, No.2, 1991, pp: 370-379
[106] L. Hong, Distributed Filtering Using Set Models. IEEE Trans. on AES, Vol. 28, No. 4, 1992, pp: 1144-1152
[107] L. Hong, Adaptive Distributed Filtering in Multicoordinated Systems. IEEE Trans. on AES, Vol. 27, No. 4, 1991, pp: 715-724
[108] He You, Peng Yingning, Lu Dajin, Composite Filtering in Hybrid Multisensor Data Systems, InerRader Symposium, 1998, Germany, pp:745-748
[109] He You, Peng Yingning, Lu Dajin, Sensor Track Fusion with Feedback Information. 1999 Inter. Conf. on Radar Systems, France
[110] 何友等，带反馈信息的分布式多传感哭航迹融合，电子科学学刊，2000，22(2)：1-10
[111] Qiang Gan, Chis J. Harris, "Comparison of Two Measurement Fusion Methods for Kalman-Filter-Based Multisensor Data Fusion",IEEE Transaction on Aerospace and Electronic Systems, Vol. 37, pp.273-280, 2001.
[112] Roecker, J. A., and McGillem, C.D. "Comparison of two-sensor
tracking methods based on state vector fusion and measurement fusion", IEEE Transaction on Aerospace and Electronic Systems, Vol.24, pp. 447-449, 1988
[113] Willsky, A.S., Bello, M.G., Castanon, D.A., Levy, B.C., and Verghese, G.C.,. "Combining and update of local estimation and regional maps along sets of one-dimensional track", IEEE Transaction on Automatic Control, Vol. 27, pp. 799-813, 1982
[114] Chris J. Harris, Qiang Gan, State Estimation and Multi-sensor Data Fusion Using Data-based Neurofuzzy Local Linearisation Process Models, Information Fusion, 2001 (2), 17-29
[115] Huimin Chen, Thiaglingam Kirubarjan, Yaakov Bar-Shalom,Track-to-Track Fusion versus Centralized Estimation: Theory and Application, IEEE Trans.on AES, Vol.39,No.2,2003,pp:386-411
[116] 张军英，信息融合中重要性测度的区间估计，西安大学学报，1999，26(3), 332-336
[117] Yawgeng A. Chau and Evaggeilos Geranitis, Distributed Multisensor Parameter Estimation in Dependent noise, IEEE Transaction on Comminications, Vol.40, No.2, February, 1992,373-394
[118] 谢美华，王正明，多传感器跟踪目标的数据互联，中国空间科学技术，2000，第6期，1-7
[119] 胡国辉，范胜林，容错信息融合滤波算法的研究，中国惯性技术学报，1998，6(1)，20-24
[120] Zhi-Quan Luo, John N. Tsitsiklis, Data Fusion with Minimal Communication, IEEE Transaction on Information Theory, Vol. 40,No. 5, September, 1994, 1551-1563
[121] Uwe D. Hanebeck, Joachim Horn, Fusion Information Simultaneously Corrupted by Uncertainties with Known Bounds and Random Noise with Known Distribution, Information Fusion, 2000(1),55-63
[122] 徐毓，无穷范数下的目标状态融合方法，华中科技大学学报，30(1)，2002，41-43
[123] M. Haimovich, J. Yosko, Fusion of Sensors with Dissimilar Measurement/Tracking Accuracies, IEEE Transaction on Aerospace and Electronic Systems, Vol. 29, No. 1, January, 1993,245-249
[124] Liu Zuoliang, A Data Algorithm for Asynchronous Multisensor Data Fusion, Proceedings oflCSP'98, 1548-1552
[125] 宋小全，孙仲康，组网雷达在干扰条件下的目标跟踪，现代雷达，19(2)，1997，12．19
[126] 王洁等，异步多传感器数据融合，控制与决策，16(6)，2001，877-881
[127] 文成林等，多尺度动态模型单传感器动态系统分布式信息融合，自动化学报，22(2)，2001，158-165
[128] 文成林，多传感器单模型动态系统多尺度数据融合，电子学报，2001，第3期，341-345
[129] 陈隽永等，多分辨数据融合技术，系统工程与电子技术，21(1)，1999，25-32
[130] 胡战虎，李言俊，基于小波理论的多分辨率多传感器数据融合，数据采集与处理，2001，16(1)，90-93
[131] 文成林，周东华，多尺度估计理论及其应用，清华大学出版社，2002
[132] Robert Lobbia, Mark Kent, Data Fusion of Decentralized Local Tracker Outputs, IEEE Trans. on Aerospace and Electronic Systems,vol. 30, pp. 787-798, July, 1994,
[133] David A. Castanon, Demosthenis Teneketzis, Distributed estimation Algorithms for nonlinear Systems, IEEE Trans. on Automation Control, vol. 30, No.5, pp. 418-425, May, 1985.
[134] Yee-Ming Chen, Huang-Che Huang, Multisensor Data Fusion for Manoeuvring Target Tracking, International Journal of Systems Science, Vol. 32, No. 2, 2001, pp.205-214
[135] 李勇智，李国栋，多维位置数据最优融合方法，青岛大学学报，11(3)，1998，65-69
[136] 李启虎，相关观测资料的最佳线性数据融合，声学学报，26(5)，2001，385-388
[137] 涂国平，多传感器数据融合的稳健处理方法稳健处理方法，数据采集与处理，13(1)，1998，85-87
[138] 孙占华，雷达导航避碰模糊识别，2000，29(5)，466-470
[139] Jean Gordon, Edward H. Shortliffe. A Method for Managing Evidential Reasoning in a Hierarchical Hypothesis Space, Artificial Intelligence, 1985, 26, 323-357
[140] Lee T., Richards J. A., Swain P. H., Probablistic and Evidential Approaches for Multisource Data Analysis, IEEE T-GRS 1987,GZ-25(3)
[141] 张奇，顾伟林，基于Dempster-Shaper证据推理理论的ALV视觉信息融合，计算机学报，1999，22(2)，193-198
[142] 方勇，证据理论应用于多源信息融合分析，遥感技术，2000，4(2)，106-111
[143] 马继涌，高文，多通道信息融合的改进乘机规则，电子学报，1999，27(8)，1-4
[144] 李国栋等，基因不确定度量信息融合的团队一致法研究，自动化学报，1998，24(5)，681-685
[145] G.W. Ng, K. H. Ng, Sensor Management-What, Why and How,Information Fusion. 2000(1). 67-75
[146] 刘先省等，多传感器数据融合系统闭环控制模式的构成与分析，信息与控制，2000，29(2)，145-151
[147] S. Fabre, A. Appriou, X. Briottet, Presentation and Description of Two Classification Methods Using Data Fusion Based on Sensor Management, Informarion fusion, 2001(2), 49-71
[148] Lamport L, Shostak R, Pease M., The Byzantine Generals Problem, ACM Trans Program Lang Syst, 4(3), 1982:383-401
[149] Marzullo K., Tolerating Failures of Continuous- Valued sensors,ACM Trans on Computer System, 8(4), 1990:284-304
[150] 刘贵喜，杨万海，多传感器融合系统的优化冗余，西安电子利技大学学报，27(1)，2000，9-12，34
[151] 袁天鑫，最佳估计原理，国防工业出版社，1990
[152] 邓自立，最优滤波理论及其应用——现代时间序列分析方法，
哈尔滨工业大学出版社，2000，8
[153] Carlos E.de Souza and Lihua Xie, On the discrete-time bounded real lemma with application in the characterization of static state feed back H_∞controller. System & Control Letters, 1992, 18:61-71.
[154] Carlos E.de Souza, Minyue Fu and Lihua Xie, H_∞ Analysis and synthesis of Discrete-Time Systems with Time-Varying Uncertainty,IEEE Transaction on Automatic Control, 1993,38(3): 459-462.
[155] Lihua Xie and Yeng chai Soh, Robust kalman filtering for uncertain systems. Systems & Control Letters, 1994,22:123-129.
[156] Lihua Xic, Yeng chai Soh and Carlos E. de Souza, Robust Kalman filtering for uncertain discrete-time systems. IEEE Trans. on Automatic Control, 1994, 39(6): 1310-1314.
[157] Zidong,Zhi Guo and H. Unbehauen, Robust H_2/H_∞-state cstimation for discrctc-time systems with error variance constraints.IEEE Trans, on Automatic Control , 1997,42(10):1431-1435.
[158] Vri shaked and Carlos E. de, Robust minimum variance filtering. IEEE Trans. on Signal Processing, 1995, 43(11): 2474-2483.
[159] Yahali Theodor and Vri Shaked, Robust discrete-time minimum variance filtering. IEEE Trans. on Signal Processing, 1996,44(2):181-189.
[160] 俞立．鲁棒控制——线性矩阵不等式处理方法．清华大学出版社，2002．87
[161] 刘诗娜．费树岷．冯纯伯．线性不确定系统鲁棒滤波器的设计．自动化学报，2002，20(3)：50-55．
[162] 张勇．史忠科．戴冠中．周自全．离散系统的鲁棒卡尔曼滤波新方法．控制理论与应用，2000，17(4)：505-508．
[163] 吴淮宁．费元春．不确定离散系统的最优鲁棒滤波．控制理论与应用，1999，16(2)：292-296．
[164] T. Iwasaki, R. E. Skelton, All controllers for the general
H_∞ control Problem: LMI existence conditions and state space formulas, 1994, 30(8): 1307-1317
[165] HuaiZhong Li and Minyue Fu, A Linear Matrix Inequality Approach to Robust H_∞ Filtering, IEEE Trans. on Signal Processing,Vol. 45, No. 9, 1997, pp: 2338-2350
[166] Jose C. Geromel, Optimal Linear Filtering Under Parameter Uncertainty, IEEE Trans. on Signal Processing, Vol. 47, No. 1, 1999,pp: 168-175
[167] Fang Liao, LMI-Based Reliable Robust Tracking Control Against Actuator Faulte with Application to Filght Control, Procedings of the 39th IEEE Conference on Decision and Control Sydney, Australia,Decmber, 2000, pp: 3914-3919
[168] Jian Liu, Reliable Robust Minimun Variance Filtering with Sensor Failure, Proceedings of American Control Conference, Arlington, VA 2001, 1041-1046
[169] Reinaldo M. Palhares, LMI Approach to the Mixed H_2/H_∞ Filtering Design for Discrete-Time Uncertain Systems, IEEE Trans.on AES, Vol. 37, No. 1, 2001, pp: 292-296
[170] Jose C. Geromel and Mauricio C. de Oliveira, H_2/H_∞Robust Filtering for Convex Bounded Uncertain Systems, IEEE Trans. on Automatic Control, Vol. 46, No.1, 2001, pp: 100-107
[171] Ali H. Sayed, A Framework for State-Space Estimation with Uncertain Models, IEEE Trans. Automatic Control, Vol. 46, No.7,2001, pp: 998-1013
[172] Laurent EI Ghaoui and Giuseppe Calafiore, Robust Filtering for Discrete-Time Systems with bounded Noise and Paranetric Uncertainty, IEEE Trans. on Automatic Control, Vol. 46, No.7, 2001,pp: 1084-1089
[173] S.H. Jin and J. B. Park, Robust H_∞ Filtering for Polytopic
Uncertain Systems via Convex Optimisation, IEE Proc. Control Theory Appl., Vol. 148, No. 1, 2001, pp: 55-59
[174] Sumit Roy, Ronald A. Iltis, Decentralized Linear Estimation in Correlated Measurement Noise, IEEE Transaction on Aerospace and Electronic Systems. 1991, 27(6): 939-941.
[175] Cen Jing-liang, Cen Xiang-hui, Special Matrix, Qinghua University Press, Jan, 2001:390-397
[176] 孙优贤等，造纸过程建模与控制，浙江大学出版社，1993
[2] D. L. Hall., Mathematical Techniques in Multisensor Date Fusion. Artech House, Boston, London, 1992
[3] 赵忠贵等编译，多传感器融合，电子工业部二十八所，1993年2月，南京
[4] 赵忠贵编译，数据融合方法概论，电子工业部二十八所，1998年4月，南京
[5] 康耀红，，数据融合理论与应用，西安电子科技大学出版社，1997年11月，西安
[6] 何友，多目标多传感器综合算法研究，硕士论文，海军工程大学，1988年2月，武汉
[7] 王祁，聂伟，张兆礼，数据融合与智能传感器系统，传感器技术，1998，17(6)，51-53
[8] 何友，王国宏等，多传感器信息融合及其应用，电子工业山版社，2000
[9] L. Valet, G. Mauris, Ph. Bolon, A Statistical Overview of Recent Literature on Information Fusion, IEEE AESS Systems Magazine, March 2001, 7-14
[10] 黄瑛，多传感器数据融合系统的软件实现，传感器技术，17(6)，1998，9-11
[11] Eloi Bosse, Jean Roy, Stephane Paradis, Modeling and Simulation in Support of the Design of a Data Fusion System, Information Fusion, 2000(1), 27-87
[12] Anlan N. Steinberg, Data Fusion System Engineering, IEEE AESS Systems Magazine, June 2001,7-14
[13] 刘同明等，数据融合技术及其应用，国防工业出版社，1998
[14] 郭志恒，杨春英，机载多传感器两种数据融合方法探讨，电光与控制，67(3)，1997，14-19
[15] 戴筠，王建海，分布序贯最近邻多目标跟踪算法，系统工程与电子技术，1998，第7期，11-14
[16] 卢君明，李瑞棠，组网雷达自组织最小均方差数据融合算法，西安电子科技大学学报，27(2)，2000，161-165
[17] 张军英，多雷达站数据融合处理的聚类方法，计算机仿真，17(3)，2000，8-10，26
[18] Peter S. Maybeck, Robert L. Jensen, An Adaptive Extended Kalman Filter for Target Image Tracking, IEEE Transaction on Aerospace and Electronic System, Vol. AES-17, No.2, March, 1981, 173-179
[19] Yong-Jian Zheng, Bir Bhanu, Adaptive Object Detection from Multisensor Data, Proceedings of 1996 IEEE/SICE/RSI International Conference on Multisensor Fusion and Intelligent Systems, 633-640
[20] E. Jouseau, B. Dorizzi, Neural Network and Fuzzy Data Fusion. Application to an Online and Real Time Vehicle Detection System, Pattern Recognition Letters 20(1999), 97-107
[21] 刘源，谢维信，多传感器图像模糊融合算法在图像识别中的应用，西安电子科技大学学报27(1)，2000，5-8
[22] 宝音贺喜格，黄文虎，设备故障诊断的关联矩阵方法研究，振动与冲击，1999，18(1)，1-5
[23] 张雨，设备故障信息融合问题思考，长沙交通学院学报，1999，15(2)，22-29
[24] 刘燕燕等，数据融合技术在输电线网故障诊断中的应用，信息技术，2002，第8期，2-5
[25] 张彦泽，多传感器信息融合及在智能故障诊断中的应用，传感器技术，1999，18(2)，18-22
[26] 赵方，谢友柏，油液分析多技术集成的特征与信息融合，摩擦学学报，1998，18(1)，45-52
[27] 史天运等，柔性加工单元的状态监测与故障诊断研究，北京理工大学学报，1998，18(5)，567-572
[28] 董选明，裘丽华，基于BP算法的液压泵在线状态监测及故障诊断，北京航空航天大学学报，1997，22(2)，193-198
[29] Wadi, R. Balendra, An Intelligent Approach to Monitor and Control the Blanking Process, Advances in Engineering Software, 30(1999), 85-92
[30] P.G. Mathews, M. S. Shummugam, Neural-network Approach for Predicting hole quality in Reaming, International Journal of Machine Tools & Manufacture, 39(1999), 723-730
[31] Hrianmayee Vedam, Signed Digraph Based Multiple Fault diagnosis, Computer Chem. Engng. Vol. 27, 1997, 655-660
[32] Shang-liang Chen, Y. W. Jen, Data Fusion Neural Network for Tool Condition monitoring in CNC Milling Machining, International Journal of Machine Tools & Manufacture, 40(2000), 381-400
[33] 钢铁，吴林，超声检测中的多源信息融合技术与缺陷识别，1999，35(1)，11-14
[34] 庄钊文，郁文贤，王浩，信息融合技术在可靠性评估中的应用，系统工程与电子技术，2000，22(3)，75-77，80
[35] 郭呈贺等，多智能传感器的有限分散自治体系研究，机器人，1997，19(1)，28-34
[36] 罗志增，蒋静坪，基于DS理论的多信息融合方法及应用，电子学报，1999，27(9)，100-102
[37] 王祁，聂伟，基于信息融合技术的气体识别方法的研究，机器人，1999，21(4)，288-293
[38] 王祁，聂伟，张兆礼，数据融合与智能传感系统，传感器技术，17(6)，1998，51-53
[39] 王志武等，多传感器数据融合在切割机器人系统中的应用，上海交通大学学报，36(7)，2002，995-998
[40] Soo-Chang Pei and Lin-Gwo Lion, Vehicle-type Motion Estimation by the Fusion of Image Point and Line Features, Pattern Recognition, Vol.31, No.3, pp. 333-344, 1999
[41] L. Vergara, P. Bernabeu, Automatic Signal Detection Applied to Fire Control by Infrared Digital Signal Processing, Signal Processing, 80(2000), 659-669
[42] X. Dai, S. Khorram, Data Fusion Using Artificial Neural Network:
a Case Study on Multitemporal Cjange Analysis, Computer, Environment and Urban Systems, 23(1999), 19-31
[43] P. Arena, S. Baglio, Analog Cellular Networks for Multisenson Fusion and Control, IEEE Transaction on Circuits and Systems-Ⅰ: Fundamental Theory and Application, Vol. 47, No.9, September, 2000, 1378-1382
[44] 张九龙，模式识别的最大熵方法，2000，29(4)，152-156
[45] 艾克武，综合集成的内容与方法——复杂巨系统问题研究，系统工程与电子技术，1998，7，18-23
[46] Robert R. Tenney, Nils R. Sandell, Detection with Distributed Sensors, IEEE Transaction on Aerospace and Electronic System, Vol.AES, No.4, July, 1981, 501-510
[47] Z.Char, P.K. Varshney, Optimal Data Fusion in Multiple Sensor Detection System, IEEE Transaction on Aerospace and Electronic System, Vol. AES-22, No.1, January, 1986, 98-101
[48] A.R. Reilman, L.W. Nolte, Design and Performance Comparison of Distributed Detection Networks, IEEE Transaction on Aerospace and Electronic Systems, Vol. AES-23, No.6, November, 1987,789-797
[49] C.C. Lee, J. J. Chao, Optimaim Local Decision Space Partitioning for Distributed Detection, IEEE Transaction on Aerospace and Electronic Systems, Vol. 25, No.4, July, 1989, 538-544
[50] S.C.A. Thomopoulos, R.Viswanathan, D.K. Bougoulias, Optimal Distributed Detection Fusion, IEEE Transaction on Aerospace and Electronic Systems, Vol. 25, No.5, September, 1989, 761-765
[51] Moirad Barkat, Pramod K. Varshney, Decentralized CFAR Signal Detection, IEEE Transaction on Aerospace and Electronic Systems,Vol. 25, No.2, March, 1989, 141-149
[52] Guan Jian, He You, Peng Ying-Ning, Distributed CFAR Detector Based on Local Test Statistic, Signal Processing, 80(2000), 373-379
[53] Hermann Rohling, Radar CFAR Thresholding in Clutter and Multiple Target Situations, IEEE Transaction on Aerospace and
Electronic System, Vol. AES-19, No.4, July, 1983, 608-620
[54] Y. Bar-Shalom, H. M. Shertukde, K. R. Pattipati, Use of Measurements from an Imaging sensor for Precision Target Tracking,IEEE Transaction on Aerospace and Electronic Systems, Vol. 25,No.6, November, 1989, 863-871
[55] Evaggelos Geraniotis and Yawgeng A. Chau Distributed Detection of Weak Signals from Multiple Sensors with Correlated Observations, Proceedings of 27th Conference on Decision and Comrol, 1988, 2501-2506
[56] Yawgeng A. Chau and Evaggelos Geraniotis, Asymptotically Optimal Quantization and Fusion in Multiple Sensor Systems,Proceedings of 28th Conference on Decision and Control, 1989,585-587
[57] Yawgeng A. Chau and Evaggelos Geraniotis, Multi-sensor Correlation and Quantization in Distributed Detection Systems,Proceedings of 29th Conference on Decision and Control, 1990,2692-2097
[58] Wet Chang, Mosre Kam, Asynchronous Distributed Detection,IEEE Transaction on Aerospace and Electronic Systems, Vol. 30,No.3, July, 1994, 818-826
[59] Evaggelos Geranitis, Yawgeng A. Chau, Robust Data Fusion for Multisensor Detection Systems, IEEE Transaction on Information Theory, Vol. 36, No. 6, November, 1990, 1265-179
[60] Venugopal V. Veeravalli, Tamer Baser, Minimax Robust Decentralized Detection, IEEE Transaction on Information Theory,Vol. 40. No. 1. January. 1994. 35-40
[61] 苏慧敏等，多传感器数据融合的鲁棒检测技术，北京航空航天大学学报，1999，25(2)，156-159
[62] Allen J. Kanyuck, Robert A. Singer, Correlation of Multiple-Site Track Data, IEEE Transaction on Aerospace and Electronic System,Vol. AES-6, No.2, March, 1970, 180-187
[63] J.J. Stein, S.S. Blackman, Generalized Correlation of Multi-Target
Track Data, IEEE Transaction on Aerospace and Electronic System,Vol. AES-11, No.6, November, 1975, 1207-1217
[64] S.S. Blackman, Multiple-Target Target with Radar Application,Artech House, Norwood, MA, 1986
[65] Y. Bar-Shalom, T. Fortman, Tracking and Data Association,Academic Press, New York, 1998
[66] G.Y. Trunk, J.D. Wilson, Association of DF Bearing Measurement with Radar Tracks, IEEE Transaction on Aerospace and Electronic Systems, Vol. AES-23, No.4, July, 1987, 438-447
[67] Zhou, N. K. Bose, Multitarget Tracking in Clutter: Fast Algorithms for Data Assocition, IEEE Transaction on Aerospace and Electronic Systems, Vol. 29, No.2, April, 1993, 352-363
[68] J.A. Roecker, A Class of Near Optimal JPDA Algorithms, IEEE Transaction on Aerospace and Electronic Systems, Vol. 30, No.2,April, 1994, 504-510
[69] Fitzgerald R J. Development of Practical PDA Logic for Multitarget Tracking by Microprocessor, Proceedings of the American Controls Conference, Seattle, WA, June 1986, 889-893
[70] S. Mori, C. Harris, E. Rogers, Utilizing Fuzzy Models in the Design of Estimators and Predictors: an Agile Target Tracking Example, in Proceedings if the 2nd IEEE Conference on Fuzzy Systems, 1993, 679-684
[71] Y. Bar-Shalom, Multitarget Multisensor Tracking: Applications and Adveances, Vol. Ⅱ, Artech House, Norwood, MA, 1992
[72] Sengupta, R. Illtis, Neural Solution to the Multiple Target Tracking Data Association Problem, IEEE Trans. Aerospace Electron.System AES-25. 1989. 96-108
[73] 杨新星，焦李成，一种快速全局优化的神经网络及其在数据融合中的应用，电子科学学刊，1999，21(6)，820-824
[74] R.P. Singh, W. H. Bailey, Fuzzy Logic Applications to Multisensor-multitarget Correlation, IEEE Trans. Aerospace Electron.System AES-25, 1997, 752-769
[75] Y.M. Chen, H.C. Huang, Fuzzy Logic Approach to Mulrisensor Data Association, Mathematics and Computer in Simulation,52(2000), 399-412
[76] Ashraf M. Aziz, Murali Tummala, Roberto Cristi, Fuzzy Logic Data Correlation Approach in Multisensor-multitarget Tracking Systems, Signal Processing, 76(1999), 195-209
[77] 刘源，谢维信，基于多传感器多目标特征信息的模糊数据关联算法，系统工程与电子技术，1998，第12期，18-23
[78] 王明辉等，强干扰环境下性能优化的相互作用多模型-概率数据互联算法，自动化学报，27(2)，2001，267-270
[79] Singer R. and A. J. Kanyuck. Computer control of multiple site track correlation, Automation, Vol. 7, pp. 455-464, 1971
[80] D. Willner, C.B. Chang, K. P. Duunn, Kahman Filter Algorithm for a Multisensor System. In Proc IEEE Conf. Decision and Control.Dec. 1976
[81] M.F. Hassan, et al., A Decentralized Computational algorithm for the Global Kalman Filler. IEEE Trans. On Automat. Contr., Vo.,AC-23, 1978, pp262-268
[82] C.Y. Chong, Hierarchical Estimation. MIT/ONR C3 Workshop Monterey, CA, 1979
[83] Bar-shalom, Y., "On the track-to-track correlation problem", IEEE Transaction on Automatic Control, Vol. 26, pp. 571-572, 1981
[84] Bar-shalom, Y., Capmo, L., "The effect of the common process noise on the two-sensor fused-track covariance", IEEE Transaction on Aerospace and Electronic Systems, Vol. 22, pp. 803-805, 1986
[85] T. Kerr. Decentralized Filtering and Redundancy Management for Multisensor Navigation. IEEE Trans. On AES, Vol. 23, No. 1, 1987
[86] 何友，多目标多传感器综合算法，湖北省武汉地区研究生第三届学术年会，1987年第一期，1-11
[87] 何友，多传感器综合系统中的数据合成，全国C~3I年会论文，1998年11月，郑州
[88] 何友，多传感器网络中的分布估计，海军航空工程学院学报，
1988年第4期，1-8
[89] 周叶，戴冠中，王立新，线性离散时间系统分散估计的合成算法，控制与决策，1989年第1期，1-6
[90] 何友，多传感器系统中的航迹合成，火力与指挥控制，1990年第1期，7-14
[91] P.L. Liu, Local Estimation Combination for the Solving Tracking Problem. 1983 Asilomar Conf. On Circuits Systems and Computer,pp. 378-382
[92] A.S. Willsky, M.G. Bello, et al. Combining and Updating of Local Estimation and Regional Maps along Sets of One-dimensional Track. IEEE Trans. On AC, Vol. 27, No. 4, 1982, pp: 799-813
[93] Bierman B. J., Belzer M. R., A Decentrlized Square Root Information Filter/Smoother. Proceeding of 24th CDC, 1985,pp:1902-1905
[94] Neal A. Carlson, Federated Square Root Filter for Decentralized Parallel Processes, IEEE Trans. on Aerospace and Electronic Systems,vol. 26, pp. 517-525, May 1990.
[95] D.A. Castanon, et al. Nonlinear Data Fusion. In Proc. 1982 MIT/ONR Workshop Command Control, Montory, CA Aug. 1982
[96] D. A. Castanon and D. Tenekzis. Distributed Estimation Algorithm for Nonlinear Systems. IEEE Trans. on AC, Vol. 30, No.5,1985
[97] R. Lobbia, M. Kent. Data Fusion of Decentralized Local tracker Outputs. IEEE Trans. AES, Vol. 30, No.3, 1994:787-799
[98] Hamd R.Hashemipour, Summit Roy, and Alan J. Laub,"Decentralized Structure for Parallel Kalman Filtering", IEEE Transaction on Automatic Control, Vol.33, pp. 88-93, 1988
[99] K.C., Chang, Saha, R. K., Bar-Shalom, Y., "On optimal track-to-track fusion", IEEE Transaction on Aerospace and Electronic Systems, Vol. 33, pp. 1271-1276, 1997.
[100] T.H. Kerr. Comments on: Federated Square Root Filter for Decentralized Parallel Processes. IEEE Trans. on AES. Vol. 27, No. 6,
1991, pp: 946-948
[101] S. Roy, R. H. Hashem and A. J. Laub, Square Root Filter for Parallel Kalman Filtering Using Reduced-order Local Filters. IEEE trans, on AES, Vol. 27, No. 2, 1991, pp.276-288
[102] T. M. Berg, et al. General Decentralized Kalman Filters.Proceedings of the American Control Conference, Mayland, June 1994, pp: 2273-2274
[103] U.B. Desi and B. Das. Parallel algorithm for Kalman Filtering. In Proc. 1985 Amer. Contr. Conf., Boston, MA, June, 1985, pp: 920-921
[104] G. G. Meyer and H. W. Weinert. Parellel Algorithm and Computeational Structures for Linear Estimation Problems. In Statiatical Signal Processing, E. J. Wegman, New York, 1984, pp:507-516
[105] L. Hong Centealized and Distributed multisensor Integration with Uncertainties in Communication Networks. IEEE Trans. on AES, Vol.27, No.2, 1991, pp: 370-379
[106] L. Hong, Distributed Filtering Using Set Models. IEEE Trans. on AES, Vol. 28, No. 4, 1992, pp: 1144-1152
[107] L. Hong, Adaptive Distributed Filtering in Multicoordinated Systems. IEEE Trans. on AES, Vol. 27, No. 4, 1991, pp: 715-724
[108] He You, Peng Yingning, Lu Dajin, Composite Filtering in Hybrid Multisensor Data Systems, InerRader Symposium, 1998, Germany, pp:745-748
[109] He You, Peng Yingning, Lu Dajin, Sensor Track Fusion with Feedback Information. 1999 Inter. Conf. on Radar Systems, France
[110] 何友等，带反馈信息的分布式多传感哭航迹融合，电子科学学刊，2000，22(2)：1-10
[111] Qiang Gan, Chis J. Harris, "Comparison of Two Measurement Fusion Methods for Kalman-Filter-Based Multisensor Data Fusion",IEEE Transaction on Aerospace and Electronic Systems, Vol. 37, pp.273-280, 2001.
[112] Roecker, J. A., and McGillem, C.D. "Comparison of two-sensor
tracking methods based on state vector fusion and measurement fusion", IEEE Transaction on Aerospace and Electronic Systems, Vol.24, pp. 447-449, 1988
[113] Willsky, A.S., Bello, M.G., Castanon, D.A., Levy, B.C., and Verghese, G.C.,. "Combining and update of local estimation and regional maps along sets of one-dimensional track", IEEE Transaction on Automatic Control, Vol. 27, pp. 799-813, 1982
[114] Chris J. Harris, Qiang Gan, State Estimation and Multi-sensor Data Fusion Using Data-based Neurofuzzy Local Linearisation Process Models, Information Fusion, 2001 (2), 17-29
[115] Huimin Chen, Thiaglingam Kirubarjan, Yaakov Bar-Shalom,Track-to-Track Fusion versus Centralized Estimation: Theory and Application, IEEE Trans.on AES, Vol.39,No.2,2003,pp:386-411
[116] 张军英，信息融合中重要性测度的区间估计，西安大学学报，1999，26(3), 332-336
[117] Yawgeng A. Chau and Evaggeilos Geranitis, Distributed Multisensor Parameter Estimation in Dependent noise, IEEE Transaction on Comminications, Vol.40, No.2, February, 1992,373-394
[118] 谢美华，王正明，多传感器跟踪目标的数据互联，中国空间科学技术，2000，第6期，1-7
[119] 胡国辉，范胜林，容错信息融合滤波算法的研究，中国惯性技术学报，1998，6(1)，20-24
[120] Zhi-Quan Luo, John N. Tsitsiklis, Data Fusion with Minimal Communication, IEEE Transaction on Information Theory, Vol. 40,No. 5, September, 1994, 1551-1563
[121] Uwe D. Hanebeck, Joachim Horn, Fusion Information Simultaneously Corrupted by Uncertainties with Known Bounds and Random Noise with Known Distribution, Information Fusion, 2000(1),55-63
[122] 徐毓，无穷范数下的目标状态融合方法，华中科技大学学报，30(1)，2002，41-43
[123] M. Haimovich, J. Yosko, Fusion of Sensors with Dissimilar Measurement/Tracking Accuracies, IEEE Transaction on Aerospace and Electronic Systems, Vol. 29, No. 1, January, 1993,245-249
[124] Liu Zuoliang, A Data Algorithm for Asynchronous Multisensor Data Fusion, Proceedings oflCSP'98, 1548-1552
[125] 宋小全，孙仲康，组网雷达在干扰条件下的目标跟踪，现代雷达，19(2)，1997，12．19
[126] 王洁等，异步多传感器数据融合，控制与决策，16(6)，2001，877-881
[127] 文成林等，多尺度动态模型单传感器动态系统分布式信息融合，自动化学报，22(2)，2001，158-165
[128] 文成林，多传感器单模型动态系统多尺度数据融合，电子学报，2001，第3期，341-345
[129] 陈隽永等，多分辨数据融合技术，系统工程与电子技术，21(1)，1999，25-32
[130] 胡战虎，李言俊，基于小波理论的多分辨率多传感器数据融合，数据采集与处理，2001，16(1)，90-93
[131] 文成林，周东华，多尺度估计理论及其应用，清华大学出版社，2002
[132] Robert Lobbia, Mark Kent, Data Fusion of Decentralized Local Tracker Outputs, IEEE Trans. on Aerospace and Electronic Systems,vol. 30, pp. 787-798, July, 1994,
[133] David A. Castanon, Demosthenis Teneketzis, Distributed estimation Algorithms for nonlinear Systems, IEEE Trans. on Automation Control, vol. 30, No.5, pp. 418-425, May, 1985.
[134] Yee-Ming Chen, Huang-Che Huang, Multisensor Data Fusion for Manoeuvring Target Tracking, International Journal of Systems Science, Vol. 32, No. 2, 2001, pp.205-214
[135] 李勇智，李国栋，多维位置数据最优融合方法，青岛大学学报，11(3)，1998，65-69
[136] 李启虎，相关观测资料的最佳线性数据融合，声学学报，26(5)，2001，385-388
[137] 涂国平，多传感器数据融合的稳健处理方法稳健处理方法，数据采集与处理，13(1)，1998，85-87
[138] 孙占华，雷达导航避碰模糊识别，2000，29(5)，466-470
[139] Jean Gordon, Edward H. Shortliffe. A Method for Managing Evidential Reasoning in a Hierarchical Hypothesis Space, Artificial Intelligence, 1985, 26, 323-357
[140] Lee T., Richards J. A., Swain P. H., Probablistic and Evidential Approaches for Multisource Data Analysis, IEEE T-GRS 1987,GZ-25(3)
[141] 张奇，顾伟林，基于Dempster-Shaper证据推理理论的ALV视觉信息融合，计算机学报，1999，22(2)，193-198
[142] 方勇，证据理论应用于多源信息融合分析，遥感技术，2000，4(2)，106-111
[143] 马继涌，高文，多通道信息融合的改进乘机规则，电子学报，1999，27(8)，1-4
[144] 李国栋等，基因不确定度量信息融合的团队一致法研究，自动化学报，1998，24(5)，681-685
[145] G.W. Ng, K. H. Ng, Sensor Management-What, Why and How,Information Fusion. 2000(1). 67-75
[146] 刘先省等，多传感器数据融合系统闭环控制模式的构成与分析，信息与控制，2000，29(2)，145-151
[147] S. Fabre, A. Appriou, X. Briottet, Presentation and Description of Two Classification Methods Using Data Fusion Based on Sensor Management, Informarion fusion, 2001(2), 49-71
[148] Lamport L, Shostak R, Pease M., The Byzantine Generals Problem, ACM Trans Program Lang Syst, 4(3), 1982:383-401
[149] Marzullo K., Tolerating Failures of Continuous- Valued sensors,ACM Trans on Computer System, 8(4), 1990:284-304
[150] 刘贵喜，杨万海，多传感器融合系统的优化冗余，西安电子利技大学学报，27(1)，2000，9-12，34
[151] 袁天鑫，最佳估计原理，国防工业出版社，1990
[152] 邓自立，最优滤波理论及其应用——现代时间序列分析方法，
哈尔滨工业大学出版社，2000，8
[153] Carlos E.de Souza and Lihua Xie, On the discrete-time bounded real lemma with application in the characterization of static state feed back H_∞controller. System & Control Letters, 1992, 18:61-71.
[154] Carlos E.de Souza, Minyue Fu and Lihua Xie, H_∞ Analysis and synthesis of Discrete-Time Systems with Time-Varying Uncertainty,IEEE Transaction on Automatic Control, 1993,38(3): 459-462.
[155] Lihua Xie and Yeng chai Soh, Robust kalman filtering for uncertain systems. Systems & Control Letters, 1994,22:123-129.
[156] Lihua Xic, Yeng chai Soh and Carlos E. de Souza, Robust Kalman filtering for uncertain discrete-time systems. IEEE Trans. on Automatic Control, 1994, 39(6): 1310-1314.
[157] Zidong,Zhi Guo and H. Unbehauen, Robust H_2/H_∞-state cstimation for discrctc-time systems with error variance constraints.IEEE Trans, on Automatic Control , 1997,42(10):1431-1435.
[158] Vri shaked and Carlos E. de, Robust minimum variance filtering. IEEE Trans. on Signal Processing, 1995, 43(11): 2474-2483.
[159] Yahali Theodor and Vri Shaked, Robust discrete-time minimum variance filtering. IEEE Trans. on Signal Processing, 1996,44(2):181-189.
[160] 俞立．鲁棒控制——线性矩阵不等式处理方法．清华大学出版社，2002．87
[161] 刘诗娜．费树岷．冯纯伯．线性不确定系统鲁棒滤波器的设计．自动化学报，2002，20(3)：50-55．
[162] 张勇．史忠科．戴冠中．周自全．离散系统的鲁棒卡尔曼滤波新方法．控制理论与应用，2000，17(4)：505-508．
[163] 吴淮宁．费元春．不确定离散系统的最优鲁棒滤波．控制理论与应用，1999，16(2)：292-296．
[164] T. Iwasaki, R. E. Skelton, All controllers for the general
H_∞ control Problem: LMI existence conditions and state space formulas, 1994, 30(8): 1307-1317
[165] HuaiZhong Li and Minyue Fu, A Linear Matrix Inequality Approach to Robust H_∞ Filtering, IEEE Trans. on Signal Processing,Vol. 45, No. 9, 1997, pp: 2338-2350
[166] Jose C. Geromel, Optimal Linear Filtering Under Parameter Uncertainty, IEEE Trans. on Signal Processing, Vol. 47, No. 1, 1999,pp: 168-175
[167] Fang Liao, LMI-Based Reliable Robust Tracking Control Against Actuator Faulte with Application to Filght Control, Procedings of the 39th IEEE Conference on Decision and Control Sydney, Australia,Decmber, 2000, pp: 3914-3919
[168] Jian Liu, Reliable Robust Minimun Variance Filtering with Sensor Failure, Proceedings of American Control Conference, Arlington, VA 2001, 1041-1046
[169] Reinaldo M. Palhares, LMI Approach to the Mixed H_2/H_∞ Filtering Design for Discrete-Time Uncertain Systems, IEEE Trans.on AES, Vol. 37, No. 1, 2001, pp: 292-296
[170] Jose C. Geromel and Mauricio C. de Oliveira, H_2/H_∞Robust Filtering for Convex Bounded Uncertain Systems, IEEE Trans. on Automatic Control, Vol. 46, No.1, 2001, pp: 100-107
[171] Ali H. Sayed, A Framework for State-Space Estimation with Uncertain Models, IEEE Trans. Automatic Control, Vol. 46, No.7,2001, pp: 998-1013
[172] Laurent EI Ghaoui and Giuseppe Calafiore, Robust Filtering for Discrete-Time Systems with bounded Noise and Paranetric Uncertainty, IEEE Trans. on Automatic Control, Vol. 46, No.7, 2001,pp: 1084-1089
[173] S.H. Jin and J. B. Park, Robust H_∞ Filtering for Polytopic
Uncertain Systems via Convex Optimisation, IEE Proc. Control Theory Appl., Vol. 148, No. 1, 2001, pp: 55-59
[174] Sumit Roy, Ronald A. Iltis, Decentralized Linear Estimation in Correlated Measurement Noise, IEEE Transaction on Aerospace and Electronic Systems. 1991, 27(6): 939-941.
[175] Cen Jing-liang, Cen Xiang-hui, Special Matrix, Qinghua University Press, Jan, 2001:390-397
[176] 孙优贤等，造纸过程建模与控制，浙江大学出版社，1993