多目标无源跟踪数据融合算法研究
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摘要
被动雷达是电子对抗系统中重要的组成部分，已成为各国未来武器系统中重要的发展方向之一。由于目标辐射源信号和电磁波传播环境的复杂性，导致被动雷达的跟踪精度较低。若利用数据融合方法有效地融合被动雷达的不同信息，从而改善目标跟踪精度，同时提高被动雷达对多目标的分辨率。
本论文正是针对上述问题，以被动雷达数据融合算法为主要的研究对象，在深入分析影响目标跟踪的主要因素的基础上，对被动雷达数据关联、目标测量数据和目标状态融合算法作了创新性和探索性研究。本文的主要内容和创新如下：
1．深入分析了影响被动跟踪的主要因素。将PCRLB(Posterior Cramer-RaoLower Bound)理论应用于单目标被动跟踪情况中，给出了仅使用角度跟踪PCRLB的数学表达式，在表达式中考虑了过程噪声等因素。另外，这一结果被进一步推广，给出了多目标情况下的PCRLB数学表达式。最后，通过仿真直观得给出了各因素与跟踪精度下限的关系。
2．提出了被动跟踪条件下的联合概率数据关联(Join Probability DataAssociation JPDA)算法。利用被动雷达所提取的目标辐射源信号特性，对传统的JPDA算法进行了改进，提高了目标关联正确概率。在此基础上，提出了目标信号分类矩阵JPDA算法，并将该算法推广到多传感器多目标的JPDA算法中，提高被动雷达对多目标的分辨率。
3．研究了传统的测量数据融合算法，在此基础上提出了针对被动跟踪的通用测量数据融合算法。另外，结合文中提出的多目标数据关联算法，给出了多目标通用测量数据融合算法。并将此通用算法应用在三个不同的被动测量数据中，通过仿真试验与传统的测量融合算法进行比较，该算法能有效改善目标跟踪精度。
4．通过理论推导给出了基于协方差交叉状态融合的Kalman滤波算法。并将该算法与传统的状态融合算法进行了比较，新算法整体性能更优。另外，新算法与测量融合比较，在跟踪精度上接近，但计算量更小，通信量更少。同时，本文对影响算法的三个主要因素，即采样时间、系统是否反馈和传感器数量，进行了深入的分析。进一步，结合修正的K近邻域航迹关联算法，将该算法推广到多传感器多目标的情况，并给出了仿真试验证明其有效性。
As important part of electromagnetic countermeasure system, the development and application of passive radar is one of primary development directions in the future weapon systems of various nations. Due to complexity of target's radiation source signal and of electromagnetic wave parpagation environment, the tracking precision of passive radar is low. The study shows that data fusion technology can effectively fuse different information of passive radar, and thus it can improve the tracking precision and resolution of target.
In this dissertation, passive radar data fusion algorithm is studied in detail. Based on the analysis of main factors affecting the precision of target tracking, the study on passive radar data correlation, target measurement data and target state fusion algorithm is done. Main work and breakthrough are presented as follows:
1. The analysis of main factors affecting passive tracking is done. Then, Posterior Carmer-Rao Lower Bound (PCRLB) theory is applied in the passive tracking of single target, and the mathematical formula of the PCRLB only using angle is derived in which some factors such as process noise are considered. In addition, the mathematical formula of the PCRLB for multi-targets is also derived after generalization. Finally, simulation experiments are done to reveal the relation between different factors and the low bound of tracking precision for intuition.
2. The Joint Probability Data Association (JPDA) algorithm is presented. By using the characteristics of target radiation signal extracted by passive radar, the improvement on the traditional JPDA algorithm is done, in which target correlation exact probability is further improved. Then, the target signal classification matrix JPDA algorithm is presented. Furthermore, it is generalized to include the case of multi-sensors multi-targets JPDA algorithm, which can improve the tracking resolution of passive radar tracking multi-targets.
3. The traditional measurement data fusion algorithm is studied, on which the general measurement data date fusion algorithm is derived. In addition, combined with the multi-targets data correlation algorithm presented by the author, the multi-targets measurement data date fusion algorithm is presented. In order to verify the validity of the new algorithm, it is applied in the simulation experiments of three different passive measurements. The simulation results show that the new algorithm can effectively improve the target tracking precision in comparison with the traditional ones.
4. The Kalman filtering algorithm based on the covariance cross state fusion is theoretically derive in this dissertation. The comparison between the new algorithm and the traditional state fusion algorithm shows that the performance of the new one is better than that of the latter. In addition, the comparison between the new algorithm and the traditional measurement fusion algorithm shows that the computation cost and communication traffic of the new one are lower than those of the latter when the tracking precision is kept same. At the same time, three main factors affecting the algorithm is studied in detail, i.e., whether system is feedback, sampling time and the number of sensors. Furthermore, combined with the modified K neighborhood flight track correlation algorithm, the new algorithm is generalized to include the case of multi-targets. Finally, the simulation experiments are done to verify its validity.
本论文正是针对上述问题，以被动雷达数据融合算法为主要的研究对象，在深入分析影响目标跟踪的主要因素的基础上，对被动雷达数据关联、目标测量数据和目标状态融合算法作了创新性和探索性研究。本文的主要内容和创新如下：
1．深入分析了影响被动跟踪的主要因素。将PCRLB(Posterior Cramer-RaoLower Bound)理论应用于单目标被动跟踪情况中，给出了仅使用角度跟踪PCRLB的数学表达式，在表达式中考虑了过程噪声等因素。另外，这一结果被进一步推广，给出了多目标情况下的PCRLB数学表达式。最后，通过仿真直观得给出了各因素与跟踪精度下限的关系。
2．提出了被动跟踪条件下的联合概率数据关联(Join Probability DataAssociation JPDA)算法。利用被动雷达所提取的目标辐射源信号特性，对传统的JPDA算法进行了改进，提高了目标关联正确概率。在此基础上，提出了目标信号分类矩阵JPDA算法，并将该算法推广到多传感器多目标的JPDA算法中，提高被动雷达对多目标的分辨率。
3．研究了传统的测量数据融合算法，在此基础上提出了针对被动跟踪的通用测量数据融合算法。另外，结合文中提出的多目标数据关联算法，给出了多目标通用测量数据融合算法。并将此通用算法应用在三个不同的被动测量数据中，通过仿真试验与传统的测量融合算法进行比较，该算法能有效改善目标跟踪精度。
4．通过理论推导给出了基于协方差交叉状态融合的Kalman滤波算法。并将该算法与传统的状态融合算法进行了比较，新算法整体性能更优。另外，新算法与测量融合比较，在跟踪精度上接近，但计算量更小，通信量更少。同时，本文对影响算法的三个主要因素，即采样时间、系统是否反馈和传感器数量，进行了深入的分析。进一步，结合修正的K近邻域航迹关联算法，将该算法推广到多传感器多目标的情况，并给出了仿真试验证明其有效性。
As important part of electromagnetic countermeasure system, the development and application of passive radar is one of primary development directions in the future weapon systems of various nations. Due to complexity of target's radiation source signal and of electromagnetic wave parpagation environment, the tracking precision of passive radar is low. The study shows that data fusion technology can effectively fuse different information of passive radar, and thus it can improve the tracking precision and resolution of target.
In this dissertation, passive radar data fusion algorithm is studied in detail. Based on the analysis of main factors affecting the precision of target tracking, the study on passive radar data correlation, target measurement data and target state fusion algorithm is done. Main work and breakthrough are presented as follows:
1. The analysis of main factors affecting passive tracking is done. Then, Posterior Carmer-Rao Lower Bound (PCRLB) theory is applied in the passive tracking of single target, and the mathematical formula of the PCRLB only using angle is derived in which some factors such as process noise are considered. In addition, the mathematical formula of the PCRLB for multi-targets is also derived after generalization. Finally, simulation experiments are done to reveal the relation between different factors and the low bound of tracking precision for intuition.
2. The Joint Probability Data Association (JPDA) algorithm is presented. By using the characteristics of target radiation signal extracted by passive radar, the improvement on the traditional JPDA algorithm is done, in which target correlation exact probability is further improved. Then, the target signal classification matrix JPDA algorithm is presented. Furthermore, it is generalized to include the case of multi-sensors multi-targets JPDA algorithm, which can improve the tracking resolution of passive radar tracking multi-targets.
3. The traditional measurement data fusion algorithm is studied, on which the general measurement data date fusion algorithm is derived. In addition, combined with the multi-targets data correlation algorithm presented by the author, the multi-targets measurement data date fusion algorithm is presented. In order to verify the validity of the new algorithm, it is applied in the simulation experiments of three different passive measurements. The simulation results show that the new algorithm can effectively improve the target tracking precision in comparison with the traditional ones.
4. The Kalman filtering algorithm based on the covariance cross state fusion is theoretically derive in this dissertation. The comparison between the new algorithm and the traditional state fusion algorithm shows that the performance of the new one is better than that of the latter. In addition, the comparison between the new algorithm and the traditional measurement fusion algorithm shows that the computation cost and communication traffic of the new one are lower than those of the latter when the tracking precision is kept same. At the same time, three main factors affecting the algorithm is studied in detail, i.e., whether system is feedback, sampling time and the number of sensors. Furthermore, combined with the modified K neighborhood flight track correlation algorithm, the new algorithm is generalized to include the case of multi-targets. Finally, the simulation experiments are done to verify its validity.
引文
[1]何友，修建娟，张晶炜等．雷达数据处理及应川．北京：电子工业出版社，2006：170-180
[2]何友，王国宏，彭应宁等．多传感器信息融合及应用．北京：电子工业出版社，2007
[3]胡来招．无源定位．北京：国防工业出版社，2004：3-9
[4]孙仲康，周一宇，何黎显．单多基地有源无源定位技术．北京：国防工业出版社，1996．
[5]David Alberts,Richard E．Hayes．信息时代军事变革与指挥控制．北京：电子工业出版社，2005：22-34
[6]David Alberts，John Garstka．网络中心行为的基本原理及其度量．北京：国防工业出版社，2007：37-1 59
[7]David Alberts，James Moffat．网络中心战与复杂性理论．北京：电子工业出版社，2004：111-131
[8]JeffCares．分布式网络化作战．北京：北京邮电大学出版社，2006：1-8．
[9]贾玉贵．现代对空情报雷达．北京：国防工业出版社，2004：80-95
[10]王小非，张鸿海．海上网络战．北京：国防工业出版社，2006：213-217
[11]Torrieri,D.J.Statistical Theory Passive Location Systems.IEEE Trans.Aerospace and Electronic System,1984,0(2):183-198
[12]郭福成，孙仲康，安玮．利用方向角及其变化率对固定辐射源的三维单站无源定位．电子学报，2002，30(12)：1885-1887
[13]杨莘元，郑思海．基于运动辐射体rOA和DOA测量的单站被动定位算法．电子学报，1996，24(12)：66-69．
[14]邓新蒲，祁颖松，卢启中，周一宇．相位差变化率的测量方法及其测量精度分析．系统工程与电子技术，2001，23(1)：20-23．
[15]王杰贵，罗景青，尹成友，刘有军．固定单站对机动目标无源定位IMM算法．信号处理，2002，18(2)：137-140
[16]Chen,C.-K.,Gardner,W.A.Signal-selective time-difference of arrival estimation for passive location of man-made signal sources in highly corruptive environments.Ⅰ.Theory and method.IEEE Trans.Signal Processing 1992 40(5):1168-1 184
[17]Chen,C.-K.,Gardner,W.A.Signal-selective time-difference of arrival estimation for passive location of man-made signal sources in highly corruptivc environments.Ⅱ.Algorithms and performance.IEEE Trans.Signal Processing 1992 40(5):1185-1197
[18]H.L.Van Trees.Detection Estimation and Modulation Theory.Part Ⅰ,New York,Wiley,1968:66-72
[19]Sinha,A.,Kirubarajan,T.,Bar-Shalom,Y.Application of the Kalman-Levy Filter for Tracking Maneuvering Targets.IEEE Trans.Aerospace and Electronic Systems,2007 43(3):1099-1107
[20]Yu,K.K.C,Watson,N.R.,Arrillaga,J.An adaptive Kalman filter for dynamic harmonic state estimation and harmonic injection tracking.IEEE Trans.Power Delivery,2005,20(2):1577-1584
[21]Mookerjee,P.,Reifler,F.Reduced state estimators for consistent tracking of maneuvering targets.IEEE Trans.Aerospace and Electronic Systems,2005,41(2):608-619
[22]You He,Jian-Juan Xiu,Guo-Hong Wang,Jian-Hua Xiu.Theorem for the combination of bistatic radar measurements using least squares.IEEE Trans.Aerospace and Electronic Systems,2003 39(4):1441-1445
[23]Yildirim,A.,Efe,M.,Ozdemir,A.K.An Alternative Model for Target Position Estimation in Radar Processors.IEEE Signal Processing Letters,2007 14(8):548-552
[24]Raieh,R.,Goldberg,J.,Messer,H.Bearing estimation for a distributed source:modeling,inherent accuracy limitations and algorithms.IEEE Trans.Signal Processing 2000,48(2):429-441
[25]Harry L.van trees,Kristine L.bell Bayesin bounds for parameter estimation and nonlinear filtering/tracking A john Wiley& sons,inc.,publication 2007
[26]Felter,S.C.An overview of decentralized Kalman filter techniques.Proceedings of the 1990 IEEE Southern Tier Technical Conference:79-87
[27]Lee,T.S.Theory and application of adaptive fading memory Kalman filters.IEEE Trans.Circuits and Systems 1988 35(4):474-477
[28]Kramer,K.A.,Stubberud,S.C.Tracking of Multiple Target Types with a Single Neural Extended Kalman Filter.IEEE Intelligent Systems,2006 3rd International Conf:463-468
[29]Dah-Jing Jwo,Sheng-Hung Wang.Adaptive Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation.IEEE Sensors Journal,2007 7(5):778-789
[30]Chung-Ming Kuo,Shu-Chiang Chung,Po-Yi Shih.Kalman filtering based rate-constrained motion estimation for very low bit rate video coding.IEEE Trans.Circuits and Systems for Video Technology.2006 16(1):3-18
[31]Luea,M.B.,Azou,S.,Burel,G,Serbanescu,A.On exact Kalman filtering of polynomial systems.IEEE Trans.Circuits and Systems I:Regular Papers,2006 53(6):1329-1340
[32]Lawrence,R.,Kaufman,H.The Kalman Filter for the Equalization of a Digital Communications Channel.IEEE Trans.Communications 1971 19(6):1137-1141
[33]Patti,A.J.,Tekalp,A.M.,Sezan,M.I.A new motion-compensated reduced-order model Kalman filters for space-varying restoration of progressive and interlaced video.IEEE Trans.Image Processing 1998 7(4):543-554
[34]Mahler,R.P.S.Multitarget Bayes filtering via first-order multitarget moments.IEEE Trans.Aerospace and Electronic Systems,2003 39(4):1152-1178
[35]Fox,V.,Hightower,J.,Lin Liao,Schulz,D.,Borriello,G.Bayesian filtering for location estimation.IEEE Pervasive Computing,2003 2(3):24-33
[36]Lanz,O.Approximate Bayesian multibody tracking.IEEETrans.Pattern Analysis and Machine Intelligence,2006,28(9):1436-1449
[37]KWOK,C.,FOX,D.,MEILA,M.Real-time particle filters.IEEE Proceeding 2004 92(3):469-484
[38]Xinyu Xu,Baoxin Li.Adaptive Rao-Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance.IEEE Trans.Image Processing 2007 16(3):838-849
[39]Gustafsson,F.,Gunnarsson,F.,Bergrnan,N.,Forssell,U.,Jansson,J.,Karlsson,R.,Nordlund,P.-J.Particle filters for positioning,navigation,and tracking.IEEE Trans.Signal Processing 2002,50(2):425-437
[40]Maskell,S.Briers,M.,Wright,R.Horridge,P.Tracking using a radar and a problem specific proposal distribution in a particle filter.IEE Proceedings Radar,Sonar and Navigation,2005,152(5):315-322
[41]Hong L.Multiresolutional multiple.model target tracking.IEEE Trans.on Aerospace and Electronic Systems,1993,30(2):518-524
[42]Y.Bar-Shalom,T.E.Fortmann.Tracking and Data Association.Academic Press,1988
[43]Pattipati,K.R.Deb,S.Bar-Shalom Y and Washburn R.B.A New Relaxation Algorithm and Passive Sensor Data Association.1EEE Trans.Automatic Control,1992,37(2):198-213
[44]Robert L.,POP P.,K.R.Pattipati,Y.Bar-Shalom.Best S-D Assignment Algorithm with Application to Multitarget Tracking,IEEE Trans.Aerospace Electron.Systems 2001,37(1):22-37
[45]T.Kirubarajan,Y.Bar-Shalom,K.R.Pattipati.Multiassignment for Tracking a Large Number of Overlapping Objects.IEEE Trans.Aerospace Electron.Systems 2001,37(1):2-18
[46] YBar-Shalom, X.R.Li. Estimation and Tracking: Principles, Techniques and Software. Boston. MA:Artech House, 1993
[47] Nilsson N.J. Principles of artificial intelligence. Palo Alto, Calif.: Tioga, 1980
[48] Singer R. A., Sea R. G A new filter for optimal tracking in dense multitarget environment. In: proceedings of the ninth Allerton conf. circuit and system theory. Urbana-Champaign, USA:Univ. of Illinois, 1971:201-211
[49] Singer R. A., Stein J.J. An optimal tracking filter for processing sensor data of imprecisely determined origin in surveillance system. In: proceedings of the tenth IEEE conf. on decision and control. USA: Institute of electrical and electronics engineers, 1971:171-175
[50] Bar-Shalom Y, Tse E. Tracing in a cluttered environment with probability data association. Automatica, 1975(11): 451-460
[51] Bar-Shalom Y. Extension of the probability data association filter to multitarget tracking. Proc.Fifth Symp. Nonlinear Estimation, 1974:16-21
[52] Roecker J.A., Phillis GL. Suboptimal joint probability data association. IEEE Trans. AES 1993 29(2): 510-517
[53] Fisher J.L., Casasent D.P. Fast JPDA multitarget tracking algorithm. Applied Optics, 1989, 28(2): 371-376
[54] O'Neil S D., Bridgland M F. Fast algorithm for joint probability data association. In: proceeding of the SDI panels on tracking, 1991(3): 102-122
[55] Roecker J.A. A class of near optimal JPDA algorithm. IEEE Trans. on AES, 1994 30(2):504-510
[56] Reid D B. An algorithm for tracking multiple targets. IEEE trans. AC, 1979,24: 843-854
[57] Whang J H, Lee J G. Multiple Hypothesis Tracking for Maneuvering target in clutter environment. SICE'95 , Sapporo, 1995:1493-1498
[58] Danchick R., Newman G E. A fast method for finding the exact N-best hypothesis for multitarget tracking. IEEE trans, on AES, 1993,29(2): 555-560
[59] Streit R.L., Luginbuhl T. R. Maximum likelihood method for probability multi-hypothesis tracking In: Proceeding of SPIE international symposium, signal and data processing of small targets. Bellingham, WA, USA : the international society for optical engineering, 1994:394-405
[60] Streit R.L., Luginbuhl T. R. A probabilistic multi-hypothesis tracking algorithm without enumeration and pruning. In: proceeding of the sixth joint service data fusion symposium. Laurel,MD:1993:1015-1024
[61]Kanyuck A.J.,Singer R.A.Correlation of Multiple Site Track Correlation.IEEE Tracs.AES 1970,6(2):180-187
[62]Singer R.A.,Kanyuck A.J.Computer Control of Multiple Site Track Correlation.Automation,1971 (7):455-463
[63]Bar-Shalom Y.On the Track-to-Track Correlation Problem.IEEE Tracs.AES 1980,26(2):571-572
[64]何友，彭应宁，高志永等．多传感器数据融合中的两种新的航迹相关算法．电子学报，1997(9)：10-14
[65]Chang C.B.,Youens L.C.Measurement correlation for multiple sensor tracking in dense target environment.IEEE Tracs.Automatic Control 1982,27(6):1250-1252
[66]Kosaka M.,Miyamoto S.,Ihara H.A track correlation algotithm for multisensor integration.Proceding of the IEEE/AIAA 5th digital avionics system conf.1983,10(3):1-8
[67]何友，彭应宁．一种新的多目标多传感器航迹相关算法．清华大学学报，1997，9：108-113
[68]Chaffee,J.,Abel,J.GDOP and the Cramer-Rao bound.IEEE Position Location and Navigation Symposium.1994:663-668
[69]Thomas H.Kerr Status of CR-Like Lower Bounds for Nonliner Filtering.IEEE Trans.Aerospace and Electronic Systems.1989,25(5):590-600
[70]J.H.Taylor.The Cramer-Rao Estimation lower computation for deterministic nonlinear systems.IEEE Trans.on Automatic Control,1979,24(2):342-344
[71]Tichavsky,P.,Muracchik,C.H.,and Nehorai,A.Posterior Cramer-Rao bounds for discrete-time nonlinear filtering IEEE Trans.SP,1998,46(5):1386-1396
[72]Niu,R.,willett,P.K.and Bra-Shalom,Y.Matrix CRLB sealing due to measurements of uncertain origin IEEE Tran.On SP 2001,49(7):1325-1335
[73]Ristic,B.,Farina,A.,and Hernandez,M.L.Cramer-Rao lower bound for tracking multiple targets.IEE Trans.on Radar,Sonar and Navigation 2004,151(3):129-134
[74]Hue,C.,Le Cadre,J-P.Performance analysis of two sequential Monte Carlo methods and posterior Cramer-Rao bounds for multi-target tracking.In proceeding of the 5th international conf.on information fusion,Annapolis,MD,2002:464-473
[75]Ristie,B.,Zollo,S,and Arulampalam,S.Performance bounds for manoeuvring target tracking using asynchronous multi-platform angle-only measurement.In Proceedings of 4th International conference on Information Fusion,Montreal,Quebec,2001
[76]Deng X.,Zhou Y.Simulating Analysis on Estimation Error Lower Bound for Single Passive Observer Jour.Of Electronics and Information Technology 2002 24(1):54-59
[77]Tharmarasa,R.,Kirubarajan,T.,Hernandez,M.L.,Sinha,A.PCRLB-based multisensor array management for multitarget tracking.IEEE Trans Aerospace and Electronic Systems,2007,43(2):539-555
[78]Hemandez M.L.,Kirubarajan T.,Bar-Shalom Y.Multisensor resource deployment using posterior Cramer-Rao bounds.IEEE Trans Aerospace and Electronic Systems,2004,40(2):399-416
[79]Tharmarasa,R.Kirubarajan,T.,Hernandez,M.L.Large-Scale Optimal Sensor Array Management for Multitarget Tracking.IEEE Trans Systems,Man and Cybernetics,Part C:Applications and Reviews.2007,37(5):803-814
[80]Punithakumar,K.,Kirubarajan,T.,Hernandez,M.Multisensor deployment using PCRLBS,incorporating sensor deployment and motion uncertainties.IEEE Trans Aerospace and Electronic Systems,2006,42(4):1474-1485
[81]Lawrence A．Klein．多传感器数据融合理论及应用．北京：北京理工大学出版社．2004：5-60
[82]C.Hue ,J-P.Le Cadre,P.perez.Posterior Carmer-Rao bounds for multi-target tracking IEEE Trans.Aero.Elect.syst.,2006,42(1):37-49
[83]邓自立，王欣，高媛．建模与估计．北京：科学出版社．2007：198-205
[84]韩崇昭，朱洪艳，段战胜．多源信息融合．北京：清华大学出版社，2006
[85]Bar-Shalom Y.,Campo L.The effect of common process noise on the two-sensor fused-track covariance.IEEE Trans.AES 22(6),1986
[86]Kirubarajan,T.,Wang,H.,Bar-Shalom,Y.,Pattipati,K.R.Efficient multisensor fusion using multidimensional data association.IEEE Trans.Aerospace and Electronic Systems,2001,37(2):386-400
[87]Castellanos,J.A.,Neira,J.,Tardos,J.D.Multisensor fusion for simultaneous localization and map building.IEEE Trans.Robotics and Automation,2001,17(6):908-914
[88]Songhwai Oh,Schenato,L.,Chen,P.,Sastry,S.Tracking and Coordination of Multiple Agents Using Sensor Networks:System Design,Algorithms and Experiments.IEEE Proceedings 2007 95(1):234-254
[89]Lang Rong,Wei-Chang Wang,Logan,M.,Donohue,T.Multiplatform multisensor fusion with adaptive-rate data communication IEEE Trans.Aerospace and Electronic Systems,1997, 33(1):274-281
[90]Suranthiran,S.,Jayasuriya,S.Optimal fusion of multiple nonlinear sensor data.IEEE Sensors Journal,2004 4(5):651-663
[91]Pao,L.Y.,Trailovic,L.The optimal order of processing sensor information in sequential multisensor fusion algorithms.IEEE Trans.Automatic Control 2000 45(8):1532-1536
[92]申功勋，孙建锋．信息融合理论在惯性／天文／GPS组合导航系统中的应用．北京：国防工业出版社，1998：29-31
[93]Jeffrey K.Uhlmann.General data fusion for estimates with unknown cross covariances.Proc.SPIE 1996,2755:536-547
[94]Saha,R.K.,Chang,K.C.An efficient algorithm for multisensor track fusion.IEEE Trans Aerospace and Electronic Systems,1998,34(1):200-210
[95]Lewis A.Biuns,Andrew R.Cooke,Experimental study of network-centric data fusion algorithms Proc.SPIE,2003,5101:150-156
[96]Chang,K.C.,Saha,R.K.,Bar-Shalom,Y.On optimal track-to-track fusion.IEEE Trans Aerospace and Electronic Systems,1997,33(4):1271-1276
[97]李宏，徐辟，孙仲康．一种具有信息融合功能的模糊推理方法．系统工程与电子技术，1998(1)：52-54
[98]黎湘，郁文贤，庄钊文，郭桂蓉．决策层信息融合的神经网络模型与算法研究．电子学报，1997，25(9)：117-120
[99]刘勇，沈毅，胡恒章，李延秋．精确制导武器及其支持系统中的信息融合技术．系统工程与电子技术，1996(4)：1-5
[100]王积鹏，耿立贤．C31系统数据融合浅析．数据融合与专家系统专辑，南京：电子二十八所，1992
[101]权太范等．数据关联的神经模糊融合方法．电子学报，1996，24(7)：66-71
[102]Julier,S.,Uhlmann,J.,Durrant-Whyte,H.F.A new method for the nonlinear transformation of means and covariance in filters and estimators.IEEE Trans.Automatic Control,2000,45 (3):477-482
[103]Wan,E.A.,and van der Merwe,R.The unscented Kalman filter for nonlinear estimation.IEEE Proceedings of Symposium on Adaptive Systems for Signal Processing Communication and Control,Lake Louise,Alberta,Canada,Oct.2000
[104]Li,W.,Leung,H.,Yifeng Zhou.Space-time registration of radar and ESM using unscented Kalman filter IEEE Trans Aerospace and Electronic Systems,2004,40(3):824-836
[105]Zhan,R.,Wan,J.Iterated Unscented Kalman Filter for Passive Target Tracking.IEEE Trans Aerospace and Electronic Systems,2007,43(3):1155-1163
[106]邓自立，祁荣宾．多传感器信息融合次优稳态Kalman滤波器．中国学术期刊文摘(科学快报)，2000，6(3)：183-184
[107]Deng Z L,Gao Y,Mao L.New approach to information fusion steady-state Kalman filtering.Automatica 2005,41 (10):1695-1707
[108]Chen H,Kirubarajan T,Bar-Shalom Y.Performance limits of track-to-track fusion versus centralized estimation:theory and application.IEEE Trans.AES 2003,39(2):386-400
[109]Carlson N.A.Federated square toot filter for decentralized parallel processes.IEEE Trans.AES 1990,26(3):517-525
[110]Carlson N.A.Federated filter for fault tolerant integrated navigation systems.Proc.of IEEE PLANS' 88 Orlando,1988:110-119
[111]Loomis P V W,Carlson N.A,Berarducci M.P.Common Kalman filter:fault-tolerant navigation for next generation aircraft.Proc.of the Inst.of Navigation Conf.,santa barbara,CA,1988:38-45
[112]Mori S,Barker W H,Chong C.Y.Track association and track fusion with non-deterministic target dynamics.IEEE Trans.AES 2002,38(2):659-668
[113]Chang K C,Tian Z,Mori S.Performance evaluation for MAP state estimate fusion.IEEE Trans.AES 2004,40(2):706-714
[114]王宏飞，杨成梧．双被动雷达交会跟踪的精度分析与跟踪算法．电子学报，2003，31(3)：471-474
[115]徐从富，耿卫东，潘云鹤．面向数据融合的DS方法的综述．电子学报，2001，29(3)：393-396
[116]杨春玲，倪晋麟，浏国岁．转换坐标卡尔受滤波器的雷达目标跟踪．电子学报，1999，27(3)：121-123
[117]王国宏，何友．基于模糊综合和统计假设检验的雷达与ESM相关方法．系统工程与电子技术，1997(4)：13-16
[118]杨春玲，刘国岁，余英林．非线性系统中多传感器目标跟踪性能分析．电子学报，2000，28(3)：101-103
[119]刘同明．基于证据理论模糊推理的多传感器信息融合海上目标识别．软件学报，1999，10(3)：277-282
[120]康锡章．超视距目标指示系统的电子对抗能力的研究．系统工程与电子技术，1999， 21(5)：32-34
[121]郭晶，罗鹏飞，汪浩．密集杂波环境下的数据关联快速算法．航空学报，1995，19(3)：305-309
[122]周锐，申萄勋，房建成，基于信息融合的目标图像跟踪．电子学报，1998，26(12)：89-9
[123]丁赤磁毛士艺．基于雷达和成像传感器的融合跟踪．电子学报，1998，26(9)：134-138
[124]陈知远张鹏．基于多航迹处理的第三代电视跟踪算法．电子学报，2003(31)：361-364
[125]孙红岩，毛士艺，林品兴．多传感器数据分层融合的性质．电子学报，1996，24(6)：55-61
[126]尹成友，徐善驾，王东进．基于TDOA的多传感器在非对称域内的优化布局．电子学报，1998，26(12)：21-25
[127]张贤达．矩阵分析与应用．北京：清华大学出版社，2004：34-44
[128]Steven M．Kay．统计信号处理基础．北京：电子工业出版社，2003：338-364
[129]郑阿奇．Matlab实用教程．北京：电子工业出版社，2006
[2]何友，王国宏，彭应宁等．多传感器信息融合及应用．北京：电子工业出版社，2007
[3]胡来招．无源定位．北京：国防工业出版社，2004：3-9
[4]孙仲康，周一宇，何黎显．单多基地有源无源定位技术．北京：国防工业出版社，1996．
[5]David Alberts,Richard E．Hayes．信息时代军事变革与指挥控制．北京：电子工业出版社，2005：22-34
[6]David Alberts，John Garstka．网络中心行为的基本原理及其度量．北京：国防工业出版社，2007：37-1 59
[7]David Alberts，James Moffat．网络中心战与复杂性理论．北京：电子工业出版社，2004：111-131
[8]JeffCares．分布式网络化作战．北京：北京邮电大学出版社，2006：1-8．
[9]贾玉贵．现代对空情报雷达．北京：国防工业出版社，2004：80-95
[10]王小非，张鸿海．海上网络战．北京：国防工业出版社，2006：213-217
[11]Torrieri,D.J.Statistical Theory Passive Location Systems.IEEE Trans.Aerospace and Electronic System,1984,0(2):183-198
[12]郭福成，孙仲康，安玮．利用方向角及其变化率对固定辐射源的三维单站无源定位．电子学报，2002，30(12)：1885-1887
[13]杨莘元，郑思海．基于运动辐射体rOA和DOA测量的单站被动定位算法．电子学报，1996，24(12)：66-69．
[14]邓新蒲，祁颖松，卢启中，周一宇．相位差变化率的测量方法及其测量精度分析．系统工程与电子技术，2001，23(1)：20-23．
[15]王杰贵，罗景青，尹成友，刘有军．固定单站对机动目标无源定位IMM算法．信号处理，2002，18(2)：137-140
[16]Chen,C.-K.,Gardner,W.A.Signal-selective time-difference of arrival estimation for passive location of man-made signal sources in highly corruptive environments.Ⅰ.Theory and method.IEEE Trans.Signal Processing 1992 40(5):1168-1 184
[17]Chen,C.-K.,Gardner,W.A.Signal-selective time-difference of arrival estimation for passive location of man-made signal sources in highly corruptivc environments.Ⅱ.Algorithms and performance.IEEE Trans.Signal Processing 1992 40(5):1185-1197
[18]H.L.Van Trees.Detection Estimation and Modulation Theory.Part Ⅰ,New York,Wiley,1968:66-72
[19]Sinha,A.,Kirubarajan,T.,Bar-Shalom,Y.Application of the Kalman-Levy Filter for Tracking Maneuvering Targets.IEEE Trans.Aerospace and Electronic Systems,2007 43(3):1099-1107
[20]Yu,K.K.C,Watson,N.R.,Arrillaga,J.An adaptive Kalman filter for dynamic harmonic state estimation and harmonic injection tracking.IEEE Trans.Power Delivery,2005,20(2):1577-1584
[21]Mookerjee,P.,Reifler,F.Reduced state estimators for consistent tracking of maneuvering targets.IEEE Trans.Aerospace and Electronic Systems,2005,41(2):608-619
[22]You He,Jian-Juan Xiu,Guo-Hong Wang,Jian-Hua Xiu.Theorem for the combination of bistatic radar measurements using least squares.IEEE Trans.Aerospace and Electronic Systems,2003 39(4):1441-1445
[23]Yildirim,A.,Efe,M.,Ozdemir,A.K.An Alternative Model for Target Position Estimation in Radar Processors.IEEE Signal Processing Letters,2007 14(8):548-552
[24]Raieh,R.,Goldberg,J.,Messer,H.Bearing estimation for a distributed source:modeling,inherent accuracy limitations and algorithms.IEEE Trans.Signal Processing 2000,48(2):429-441
[25]Harry L.van trees,Kristine L.bell Bayesin bounds for parameter estimation and nonlinear filtering/tracking A john Wiley& sons,inc.,publication 2007
[26]Felter,S.C.An overview of decentralized Kalman filter techniques.Proceedings of the 1990 IEEE Southern Tier Technical Conference:79-87
[27]Lee,T.S.Theory and application of adaptive fading memory Kalman filters.IEEE Trans.Circuits and Systems 1988 35(4):474-477
[28]Kramer,K.A.,Stubberud,S.C.Tracking of Multiple Target Types with a Single Neural Extended Kalman Filter.IEEE Intelligent Systems,2006 3rd International Conf:463-468
[29]Dah-Jing Jwo,Sheng-Hung Wang.Adaptive Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation.IEEE Sensors Journal,2007 7(5):778-789
[30]Chung-Ming Kuo,Shu-Chiang Chung,Po-Yi Shih.Kalman filtering based rate-constrained motion estimation for very low bit rate video coding.IEEE Trans.Circuits and Systems for Video Technology.2006 16(1):3-18
[31]Luea,M.B.,Azou,S.,Burel,G,Serbanescu,A.On exact Kalman filtering of polynomial systems.IEEE Trans.Circuits and Systems I:Regular Papers,2006 53(6):1329-1340
[32]Lawrence,R.,Kaufman,H.The Kalman Filter for the Equalization of a Digital Communications Channel.IEEE Trans.Communications 1971 19(6):1137-1141
[33]Patti,A.J.,Tekalp,A.M.,Sezan,M.I.A new motion-compensated reduced-order model Kalman filters for space-varying restoration of progressive and interlaced video.IEEE Trans.Image Processing 1998 7(4):543-554
[34]Mahler,R.P.S.Multitarget Bayes filtering via first-order multitarget moments.IEEE Trans.Aerospace and Electronic Systems,2003 39(4):1152-1178
[35]Fox,V.,Hightower,J.,Lin Liao,Schulz,D.,Borriello,G.Bayesian filtering for location estimation.IEEE Pervasive Computing,2003 2(3):24-33
[36]Lanz,O.Approximate Bayesian multibody tracking.IEEETrans.Pattern Analysis and Machine Intelligence,2006,28(9):1436-1449
[37]KWOK,C.,FOX,D.,MEILA,M.Real-time particle filters.IEEE Proceeding 2004 92(3):469-484
[38]Xinyu Xu,Baoxin Li.Adaptive Rao-Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance.IEEE Trans.Image Processing 2007 16(3):838-849
[39]Gustafsson,F.,Gunnarsson,F.,Bergrnan,N.,Forssell,U.,Jansson,J.,Karlsson,R.,Nordlund,P.-J.Particle filters for positioning,navigation,and tracking.IEEE Trans.Signal Processing 2002,50(2):425-437
[40]Maskell,S.Briers,M.,Wright,R.Horridge,P.Tracking using a radar and a problem specific proposal distribution in a particle filter.IEE Proceedings Radar,Sonar and Navigation,2005,152(5):315-322
[41]Hong L.Multiresolutional multiple.model target tracking.IEEE Trans.on Aerospace and Electronic Systems,1993,30(2):518-524
[42]Y.Bar-Shalom,T.E.Fortmann.Tracking and Data Association.Academic Press,1988
[43]Pattipati,K.R.Deb,S.Bar-Shalom Y and Washburn R.B.A New Relaxation Algorithm and Passive Sensor Data Association.1EEE Trans.Automatic Control,1992,37(2):198-213
[44]Robert L.,POP P.,K.R.Pattipati,Y.Bar-Shalom.Best S-D Assignment Algorithm with Application to Multitarget Tracking,IEEE Trans.Aerospace Electron.Systems 2001,37(1):22-37
[45]T.Kirubarajan,Y.Bar-Shalom,K.R.Pattipati.Multiassignment for Tracking a Large Number of Overlapping Objects.IEEE Trans.Aerospace Electron.Systems 2001,37(1):2-18
[46] YBar-Shalom, X.R.Li. Estimation and Tracking: Principles, Techniques and Software. Boston. MA:Artech House, 1993
[47] Nilsson N.J. Principles of artificial intelligence. Palo Alto, Calif.: Tioga, 1980
[48] Singer R. A., Sea R. G A new filter for optimal tracking in dense multitarget environment. In: proceedings of the ninth Allerton conf. circuit and system theory. Urbana-Champaign, USA:Univ. of Illinois, 1971:201-211
[49] Singer R. A., Stein J.J. An optimal tracking filter for processing sensor data of imprecisely determined origin in surveillance system. In: proceedings of the tenth IEEE conf. on decision and control. USA: Institute of electrical and electronics engineers, 1971:171-175
[50] Bar-Shalom Y, Tse E. Tracing in a cluttered environment with probability data association. Automatica, 1975(11): 451-460
[51] Bar-Shalom Y. Extension of the probability data association filter to multitarget tracking. Proc.Fifth Symp. Nonlinear Estimation, 1974:16-21
[52] Roecker J.A., Phillis GL. Suboptimal joint probability data association. IEEE Trans. AES 1993 29(2): 510-517
[53] Fisher J.L., Casasent D.P. Fast JPDA multitarget tracking algorithm. Applied Optics, 1989, 28(2): 371-376
[54] O'Neil S D., Bridgland M F. Fast algorithm for joint probability data association. In: proceeding of the SDI panels on tracking, 1991(3): 102-122
[55] Roecker J.A. A class of near optimal JPDA algorithm. IEEE Trans. on AES, 1994 30(2):504-510
[56] Reid D B. An algorithm for tracking multiple targets. IEEE trans. AC, 1979,24: 843-854
[57] Whang J H, Lee J G. Multiple Hypothesis Tracking for Maneuvering target in clutter environment. SICE'95 , Sapporo, 1995:1493-1498
[58] Danchick R., Newman G E. A fast method for finding the exact N-best hypothesis for multitarget tracking. IEEE trans, on AES, 1993,29(2): 555-560
[59] Streit R.L., Luginbuhl T. R. Maximum likelihood method for probability multi-hypothesis tracking In: Proceeding of SPIE international symposium, signal and data processing of small targets. Bellingham, WA, USA : the international society for optical engineering, 1994:394-405
[60] Streit R.L., Luginbuhl T. R. A probabilistic multi-hypothesis tracking algorithm without enumeration and pruning. In: proceeding of the sixth joint service data fusion symposium. Laurel,MD:1993:1015-1024
[61]Kanyuck A.J.,Singer R.A.Correlation of Multiple Site Track Correlation.IEEE Tracs.AES 1970,6(2):180-187
[62]Singer R.A.,Kanyuck A.J.Computer Control of Multiple Site Track Correlation.Automation,1971 (7):455-463
[63]Bar-Shalom Y.On the Track-to-Track Correlation Problem.IEEE Tracs.AES 1980,26(2):571-572
[64]何友，彭应宁，高志永等．多传感器数据融合中的两种新的航迹相关算法．电子学报，1997(9)：10-14
[65]Chang C.B.,Youens L.C.Measurement correlation for multiple sensor tracking in dense target environment.IEEE Tracs.Automatic Control 1982,27(6):1250-1252
[66]Kosaka M.,Miyamoto S.,Ihara H.A track correlation algotithm for multisensor integration.Proceding of the IEEE/AIAA 5th digital avionics system conf.1983,10(3):1-8
[67]何友，彭应宁．一种新的多目标多传感器航迹相关算法．清华大学学报，1997，9：108-113
[68]Chaffee,J.,Abel,J.GDOP and the Cramer-Rao bound.IEEE Position Location and Navigation Symposium.1994:663-668
[69]Thomas H.Kerr Status of CR-Like Lower Bounds for Nonliner Filtering.IEEE Trans.Aerospace and Electronic Systems.1989,25(5):590-600
[70]J.H.Taylor.The Cramer-Rao Estimation lower computation for deterministic nonlinear systems.IEEE Trans.on Automatic Control,1979,24(2):342-344
[71]Tichavsky,P.,Muracchik,C.H.,and Nehorai,A.Posterior Cramer-Rao bounds for discrete-time nonlinear filtering IEEE Trans.SP,1998,46(5):1386-1396
[72]Niu,R.,willett,P.K.and Bra-Shalom,Y.Matrix CRLB sealing due to measurements of uncertain origin IEEE Tran.On SP 2001,49(7):1325-1335
[73]Ristic,B.,Farina,A.,and Hernandez,M.L.Cramer-Rao lower bound for tracking multiple targets.IEE Trans.on Radar,Sonar and Navigation 2004,151(3):129-134
[74]Hue,C.,Le Cadre,J-P.Performance analysis of two sequential Monte Carlo methods and posterior Cramer-Rao bounds for multi-target tracking.In proceeding of the 5th international conf.on information fusion,Annapolis,MD,2002:464-473
[75]Ristie,B.,Zollo,S,and Arulampalam,S.Performance bounds for manoeuvring target tracking using asynchronous multi-platform angle-only measurement.In Proceedings of 4th International conference on Information Fusion,Montreal,Quebec,2001
[76]Deng X.,Zhou Y.Simulating Analysis on Estimation Error Lower Bound for Single Passive Observer Jour.Of Electronics and Information Technology 2002 24(1):54-59
[77]Tharmarasa,R.,Kirubarajan,T.,Hernandez,M.L.,Sinha,A.PCRLB-based multisensor array management for multitarget tracking.IEEE Trans Aerospace and Electronic Systems,2007,43(2):539-555
[78]Hemandez M.L.,Kirubarajan T.,Bar-Shalom Y.Multisensor resource deployment using posterior Cramer-Rao bounds.IEEE Trans Aerospace and Electronic Systems,2004,40(2):399-416
[79]Tharmarasa,R.Kirubarajan,T.,Hernandez,M.L.Large-Scale Optimal Sensor Array Management for Multitarget Tracking.IEEE Trans Systems,Man and Cybernetics,Part C:Applications and Reviews.2007,37(5):803-814
[80]Punithakumar,K.,Kirubarajan,T.,Hernandez,M.Multisensor deployment using PCRLBS,incorporating sensor deployment and motion uncertainties.IEEE Trans Aerospace and Electronic Systems,2006,42(4):1474-1485
[81]Lawrence A．Klein．多传感器数据融合理论及应用．北京：北京理工大学出版社．2004：5-60
[82]C.Hue ,J-P.Le Cadre,P.perez.Posterior Carmer-Rao bounds for multi-target tracking IEEE Trans.Aero.Elect.syst.,2006,42(1):37-49
[83]邓自立，王欣，高媛．建模与估计．北京：科学出版社．2007：198-205
[84]韩崇昭，朱洪艳，段战胜．多源信息融合．北京：清华大学出版社，2006
[85]Bar-Shalom Y.,Campo L.The effect of common process noise on the two-sensor fused-track covariance.IEEE Trans.AES 22(6),1986
[86]Kirubarajan,T.,Wang,H.,Bar-Shalom,Y.,Pattipati,K.R.Efficient multisensor fusion using multidimensional data association.IEEE Trans.Aerospace and Electronic Systems,2001,37(2):386-400
[87]Castellanos,J.A.,Neira,J.,Tardos,J.D.Multisensor fusion for simultaneous localization and map building.IEEE Trans.Robotics and Automation,2001,17(6):908-914
[88]Songhwai Oh,Schenato,L.,Chen,P.,Sastry,S.Tracking and Coordination of Multiple Agents Using Sensor Networks:System Design,Algorithms and Experiments.IEEE Proceedings 2007 95(1):234-254
[89]Lang Rong,Wei-Chang Wang,Logan,M.,Donohue,T.Multiplatform multisensor fusion with adaptive-rate data communication IEEE Trans.Aerospace and Electronic Systems,1997, 33(1):274-281
[90]Suranthiran,S.,Jayasuriya,S.Optimal fusion of multiple nonlinear sensor data.IEEE Sensors Journal,2004 4(5):651-663
[91]Pao,L.Y.,Trailovic,L.The optimal order of processing sensor information in sequential multisensor fusion algorithms.IEEE Trans.Automatic Control 2000 45(8):1532-1536
[92]申功勋，孙建锋．信息融合理论在惯性／天文／GPS组合导航系统中的应用．北京：国防工业出版社，1998：29-31
[93]Jeffrey K.Uhlmann.General data fusion for estimates with unknown cross covariances.Proc.SPIE 1996,2755:536-547
[94]Saha,R.K.,Chang,K.C.An efficient algorithm for multisensor track fusion.IEEE Trans Aerospace and Electronic Systems,1998,34(1):200-210
[95]Lewis A.Biuns,Andrew R.Cooke,Experimental study of network-centric data fusion algorithms Proc.SPIE,2003,5101:150-156
[96]Chang,K.C.,Saha,R.K.,Bar-Shalom,Y.On optimal track-to-track fusion.IEEE Trans Aerospace and Electronic Systems,1997,33(4):1271-1276
[97]李宏，徐辟，孙仲康．一种具有信息融合功能的模糊推理方法．系统工程与电子技术，1998(1)：52-54
[98]黎湘，郁文贤，庄钊文，郭桂蓉．决策层信息融合的神经网络模型与算法研究．电子学报，1997，25(9)：117-120
[99]刘勇，沈毅，胡恒章，李延秋．精确制导武器及其支持系统中的信息融合技术．系统工程与电子技术，1996(4)：1-5
[100]王积鹏，耿立贤．C31系统数据融合浅析．数据融合与专家系统专辑，南京：电子二十八所，1992
[101]权太范等．数据关联的神经模糊融合方法．电子学报，1996，24(7)：66-71
[102]Julier,S.,Uhlmann,J.,Durrant-Whyte,H.F.A new method for the nonlinear transformation of means and covariance in filters and estimators.IEEE Trans.Automatic Control,2000,45 (3):477-482
[103]Wan,E.A.,and van der Merwe,R.The unscented Kalman filter for nonlinear estimation.IEEE Proceedings of Symposium on Adaptive Systems for Signal Processing Communication and Control,Lake Louise,Alberta,Canada,Oct.2000
[104]Li,W.,Leung,H.,Yifeng Zhou.Space-time registration of radar and ESM using unscented Kalman filter IEEE Trans Aerospace and Electronic Systems,2004,40(3):824-836
[105]Zhan,R.,Wan,J.Iterated Unscented Kalman Filter for Passive Target Tracking.IEEE Trans Aerospace and Electronic Systems,2007,43(3):1155-1163
[106]邓自立，祁荣宾．多传感器信息融合次优稳态Kalman滤波器．中国学术期刊文摘(科学快报)，2000，6(3)：183-184
[107]Deng Z L,Gao Y,Mao L.New approach to information fusion steady-state Kalman filtering.Automatica 2005,41 (10):1695-1707
[108]Chen H,Kirubarajan T,Bar-Shalom Y.Performance limits of track-to-track fusion versus centralized estimation:theory and application.IEEE Trans.AES 2003,39(2):386-400
[109]Carlson N.A.Federated square toot filter for decentralized parallel processes.IEEE Trans.AES 1990,26(3):517-525
[110]Carlson N.A.Federated filter for fault tolerant integrated navigation systems.Proc.of IEEE PLANS' 88 Orlando,1988:110-119
[111]Loomis P V W,Carlson N.A,Berarducci M.P.Common Kalman filter:fault-tolerant navigation for next generation aircraft.Proc.of the Inst.of Navigation Conf.,santa barbara,CA,1988:38-45
[112]Mori S,Barker W H,Chong C.Y.Track association and track fusion with non-deterministic target dynamics.IEEE Trans.AES 2002,38(2):659-668
[113]Chang K C,Tian Z,Mori S.Performance evaluation for MAP state estimate fusion.IEEE Trans.AES 2004,40(2):706-714
[114]王宏飞，杨成梧．双被动雷达交会跟踪的精度分析与跟踪算法．电子学报，2003，31(3)：471-474
[115]徐从富，耿卫东，潘云鹤．面向数据融合的DS方法的综述．电子学报，2001，29(3)：393-396
[116]杨春玲，倪晋麟，浏国岁．转换坐标卡尔受滤波器的雷达目标跟踪．电子学报，1999，27(3)：121-123
[117]王国宏，何友．基于模糊综合和统计假设检验的雷达与ESM相关方法．系统工程与电子技术，1997(4)：13-16
[118]杨春玲，刘国岁，余英林．非线性系统中多传感器目标跟踪性能分析．电子学报，2000，28(3)：101-103
[119]刘同明．基于证据理论模糊推理的多传感器信息融合海上目标识别．软件学报，1999，10(3)：277-282
[120]康锡章．超视距目标指示系统的电子对抗能力的研究．系统工程与电子技术，1999， 21(5)：32-34
[121]郭晶，罗鹏飞，汪浩．密集杂波环境下的数据关联快速算法．航空学报，1995，19(3)：305-309
[122]周锐，申萄勋，房建成，基于信息融合的目标图像跟踪．电子学报，1998，26(12)：89-9
[123]丁赤磁毛士艺．基于雷达和成像传感器的融合跟踪．电子学报，1998，26(9)：134-138
[124]陈知远张鹏．基于多航迹处理的第三代电视跟踪算法．电子学报，2003(31)：361-364
[125]孙红岩，毛士艺，林品兴．多传感器数据分层融合的性质．电子学报，1996，24(6)：55-61
[126]尹成友，徐善驾，王东进．基于TDOA的多传感器在非对称域内的优化布局．电子学报，1998，26(12)：21-25
[127]张贤达．矩阵分析与应用．北京：清华大学出版社，2004：34-44
[128]Steven M．Kay．统计信号处理基础．北京：电子工业出版社，2003：338-364
[129]郑阿奇．Matlab实用教程．北京：电子工业出版社，2006