警戒雷达组网的几个关键技术研究
|
摘要
本论文研究了警戒雷达组网中的几个关键技术问题。
第一章对雷达组网的国内外研究现状、特点和关键技术进行了系统的阐述。
第二章研究了警戒雷达组网的布站和数据融合管理的问题。首先提出并定义了布站效率的概念,推导了目标发现概率、虚警率和雷达到目标之间距离的关系式,在此基础上提出布站原则,并且给出了警戒雷达网优化布站的数学模型。为了解决优化布站数学模型中的复杂曲面寻优问题,提出了一种基于多群体搜索的实数遗传算法,该算法具有计算量较小,搜索精度高,收敛速度快,抗早熟能力强,具有一定的搜索多个全局最优点参数的能力,并且非常适合于并行搜索寻优。随后应用布站原则对点目标和给定的RCS曲线目标,使用相同威力范围的雷达进行了纵深部署和前沿部署陆基警戒雷达组网的优化布站仿真研究,给出了有意义的结果。最后应用布站数学模型对给定雷达网覆盖区域、雷达站布放位置和最小目标发现概率的条件下选择雷达的问题进行了仿真计算。在数据融合管理的问题方面,提出了一种具有复杂拓扑结构的数据融合管理网络。分析了实现该网络的关键问题,提出并定义了数据融合子网的性能代价函数,在此基础上给出了选择最优数据融合子网规模的准则,并且进行了仿真实验。
第三章分析了警戒雷达组网数据融合的特点。严格地推导了探测数据的坐标转换、探测均方误差的坐标转换和融合权重计算公式。针对雷达网中雷达站布站产生的误差提出了一种基于LMS的雷达网系统误差校正方法。为了适应警戒雷达网目标跟踪的需要,提出了改进的自适应Kalmen滤波跟踪算法,并且为了不同的应用目的还提出了一种SWLMS(短窗LMS)滤波算法。应用仿真实验对所有的算法进行了验证。
第四章对警戒雷达组网的目标识别进行了研究。首先分析了应用已知的目标RCS曲线和低分辨雷达进行目标识别所存在的问题。依据信息熵的理论提出了一种将探测数据、RCS样本曲线通过可能性概率进行匹配的目标识别方法。最后进行了仿真实验,仿真实验证明警戒雷达网使用该方法进行目标识别是有效的。
第五章总结了本文所做的工作,并且提出了关于雷达组网更深入研究的建议。
The intent of this dissertation is to investigate several key technical problems in surveillance radar netting.
In the first chapter, there are brief reviews and comments of the research background as well as the present situation of the subject, and then the characteristic feature and key technical problems in radar netting are introduced.
The second chapter studies surveillance radar netting and sensor management of data fusion. Firstly, there are deductions of target detection probability, false alarm probability and relation formula of radar-target range. Then the conceptions of station distribution efficiency and distribution principle are proposed and defined. Based on the conclusion we develop an optimization model of surveillance radar netting. Also, a real genetic algorithm based on multi species is proposed for the complicated surface optimization in radar netting model. This algorithm has merits of less computation, higher search veracity, rapider convergence, stronger ability against precocity, and can search multi global optimal points at the same time, and also suits for parallel optimal searching. By applying the station distrbution principle to point targets and targets with given RCS, simulation researches are performed on optimal deploy problems of ground-based surveillance radar net, which contains both depth deploying and front deploying radars with same power range. The simulation shows that this method is feasible and effective. Lastly, based on the radar netting model, simulation calculations are done for the radar choosing under the condition of given radar net cover region, radar station distribution and minimal target detection probability. On the aspect of data fusion management, firstly, a data fusion management network with complicated topological structure is proposed, and the key problems for the network realization are analyzed. Then data fusion subnet performance cost function is proposed and defined. On this basis optimization rule for data fusion subnet size choosing is proposed, and the simulation test is performed with satisfactory results.
The third chapter is about characteristic features of data fusion in surveillance radar netting. First, there are rigorous equations for fusion weight calculation, data coordinate transformation and coordinate transformation in detection mean square error. Then considering the system error produced by radar station distribution, radar net system error-correction method based on LMS is proposed. To meet the requirement of target tracking in surveillance radar net, an improved adaptive kalman filtering in tracking calculation is also proposed, as well as an SWLMS filtering algorithm for varied applications. The validity of the presented algorithms is demonstrated with simulated data at last.
The fourth chapter focuses on the target recognition in surveillance radar netting. Firstly, existence problems of target recognition based on low-resolution radar and prior target RCS curve are examined. Then on the basis of information intelligence quotient, we develop a new target recognition method, which matches the detective data and RCS sample curve though feasible probability. The validity of the presented method is demonstrated with simulated data at last.
The fifth and last chapter concludes the current trends and future outlook in the subject. In particular, some significant and valuable problems of multisensor radar netting are pointed out for further research.
第一章对雷达组网的国内外研究现状、特点和关键技术进行了系统的阐述。
第二章研究了警戒雷达组网的布站和数据融合管理的问题。首先提出并定义了布站效率的概念,推导了目标发现概率、虚警率和雷达到目标之间距离的关系式,在此基础上提出布站原则,并且给出了警戒雷达网优化布站的数学模型。为了解决优化布站数学模型中的复杂曲面寻优问题,提出了一种基于多群体搜索的实数遗传算法,该算法具有计算量较小,搜索精度高,收敛速度快,抗早熟能力强,具有一定的搜索多个全局最优点参数的能力,并且非常适合于并行搜索寻优。随后应用布站原则对点目标和给定的RCS曲线目标,使用相同威力范围的雷达进行了纵深部署和前沿部署陆基警戒雷达组网的优化布站仿真研究,给出了有意义的结果。最后应用布站数学模型对给定雷达网覆盖区域、雷达站布放位置和最小目标发现概率的条件下选择雷达的问题进行了仿真计算。在数据融合管理的问题方面,提出了一种具有复杂拓扑结构的数据融合管理网络。分析了实现该网络的关键问题,提出并定义了数据融合子网的性能代价函数,在此基础上给出了选择最优数据融合子网规模的准则,并且进行了仿真实验。
第三章分析了警戒雷达组网数据融合的特点。严格地推导了探测数据的坐标转换、探测均方误差的坐标转换和融合权重计算公式。针对雷达网中雷达站布站产生的误差提出了一种基于LMS的雷达网系统误差校正方法。为了适应警戒雷达网目标跟踪的需要,提出了改进的自适应Kalmen滤波跟踪算法,并且为了不同的应用目的还提出了一种SWLMS(短窗LMS)滤波算法。应用仿真实验对所有的算法进行了验证。
第四章对警戒雷达组网的目标识别进行了研究。首先分析了应用已知的目标RCS曲线和低分辨雷达进行目标识别所存在的问题。依据信息熵的理论提出了一种将探测数据、RCS样本曲线通过可能性概率进行匹配的目标识别方法。最后进行了仿真实验,仿真实验证明警戒雷达网使用该方法进行目标识别是有效的。
第五章总结了本文所做的工作,并且提出了关于雷达组网更深入研究的建议。
The intent of this dissertation is to investigate several key technical problems in surveillance radar netting.
In the first chapter, there are brief reviews and comments of the research background as well as the present situation of the subject, and then the characteristic feature and key technical problems in radar netting are introduced.
The second chapter studies surveillance radar netting and sensor management of data fusion. Firstly, there are deductions of target detection probability, false alarm probability and relation formula of radar-target range. Then the conceptions of station distribution efficiency and distribution principle are proposed and defined. Based on the conclusion we develop an optimization model of surveillance radar netting. Also, a real genetic algorithm based on multi species is proposed for the complicated surface optimization in radar netting model. This algorithm has merits of less computation, higher search veracity, rapider convergence, stronger ability against precocity, and can search multi global optimal points at the same time, and also suits for parallel optimal searching. By applying the station distrbution principle to point targets and targets with given RCS, simulation researches are performed on optimal deploy problems of ground-based surveillance radar net, which contains both depth deploying and front deploying radars with same power range. The simulation shows that this method is feasible and effective. Lastly, based on the radar netting model, simulation calculations are done for the radar choosing under the condition of given radar net cover region, radar station distribution and minimal target detection probability. On the aspect of data fusion management, firstly, a data fusion management network with complicated topological structure is proposed, and the key problems for the network realization are analyzed. Then data fusion subnet performance cost function is proposed and defined. On this basis optimization rule for data fusion subnet size choosing is proposed, and the simulation test is performed with satisfactory results.
The third chapter is about characteristic features of data fusion in surveillance radar netting. First, there are rigorous equations for fusion weight calculation, data coordinate transformation and coordinate transformation in detection mean square error. Then considering the system error produced by radar station distribution, radar net system error-correction method based on LMS is proposed. To meet the requirement of target tracking in surveillance radar net, an improved adaptive kalman filtering in tracking calculation is also proposed, as well as an SWLMS filtering algorithm for varied applications. The validity of the presented algorithms is demonstrated with simulated data at last.
The fourth chapter focuses on the target recognition in surveillance radar netting. Firstly, existence problems of target recognition based on low-resolution radar and prior target RCS curve are examined. Then on the basis of information intelligence quotient, we develop a new target recognition method, which matches the detective data and RCS sample curve though feasible probability. The validity of the presented method is demonstrated with simulated data at last.
The fifth and last chapter concludes the current trends and future outlook in the subject. In particular, some significant and valuable problems of multisensor radar netting are pointed out for further research.
引文
[1]李祖新,雷达对抗面临严重挑战,舰船电子对抗,1999年,第1期,pp.5-8
[2]陈振邦,低截获概率雷达技术研究,舰船电子对抗,1997年,第1期,pp.7-16
[3]陆林根,多雷达联网目标检测能力研究,系统工程与电子技术,1997年,第10期,pp.37-42
[4]Fanna A.,Studer F.A.,Radar and sensor Netting Present and Future,Microwave Jounal,1998(1),pp.97-100
[5]Sirnvsan R.,Distributed Radar Detection Theory,IEE.P.pt F.,1986,pp.133 - 60
[6]Addio E.D.,Farina A.,Overview of Detection Theory in Multistatic Radar,IEE P pt.F.,1986,pp.133
[7]Hoballah I.,Varshney P.K.,Distributed Bayesion Signal Detection Theory,IEEE Trans.on IT,1989,pp.35
[8]Victor S.C.,Fundamentals of Multisite Radar Systems Multistatic Radars and Multiradar Systems,Gordon and Breach Science Publishers,1998(Originally Published in Russian in 1993)
[9]Deley G.W.,Ballantine J.H.,Tactical Forward Area Surveillance and Control Internetting Study,Final Report,Volume I,Summary,AD-A067935,1978
[10]Blaricum G.F.Van.,Tactical Air Surveillance Radar Nettin(TASRAN)Simulator/Emulator,AD - A110382,1982
[11]Fisher W.L.,Mueche C.E.,Cameron A.G.,Registration Errors in A Netted Surveillance System,MIT Lincoln Laboratory Technical Note,1980-40,Lexington,1980
[12]Grindlay A.,Radar Bias Error Removal Algorithm for A Multiple-Site System,NRL Report 8467,Naval Research Laboratory,Washington,D.C.,1981
[13]Prather D.D.,Estimation of Radar Range Biases at Multiple Site,NRL Report 8510,Naval Research Laboratory,Washington,D.C.,1981
[14]Dana M.P.,Registration:A Prerequisite for Multiple Sensor Tracking In:Multitaget Multisensor Tracking:Advanced Applications, Artech House Inc. 1990
[15] Roy S., Iltis R. A., Decentralized Linear Estimation in Correlated Measurement Noise, IEEE Trans. Aerospace Electron. System, 1991, Vol.27(6),pp. 939-941
[16] Dela Cruz E. J., Alouani A. T., Rice T. R. , Blair W. D., Multisensor Registration, SPIE Conference on Signal and Data Processing of Small Target ' 92, 1992
[17] Dela Cruz E. J., Sensor Registration in Multisensor Systems, Master of Science Thesis, Electrical Engineering Department, Tennessee Technological University, 1992
[18] Dela E. J., Alouani A. T., Rice T. R. , Blair W. D., On Multisensor Track Alignment, IEEE Proceedings of the 32th conference on Decision and Control Tucson, Artzons, 1992
[19] Heliick R. E., Rice A., Removal of Alignment Errors in An Integrated System of Tow 3D Sensors, IEEE Trans. Aerospace Electron. System, 1993,Vol.27(4), pp. 1333-1343
[20] Bas A. , Doom V., Blom A. P., Systematic ERROR Estimation in Multisensor Fusion Systems, SPIE 1954, 1995, pp.450 - 461
[21] Leung H., Gault K., Comparison of Registration Error Correction Techniques for Air Surveillance Radar Network, In: SPIE Proc. Signal and Data Processing of Small Target, San Diego, CA, 1995, pp.498 - 508
[22] Dimitris E. M, Maurice E.C., Effect in Range Difference Position Estimation Due to Stations' Position Error, IEEE Trans. Aerospace Electron.System, 1998, Vol. 34(1), pp. 329 - 334
[23] Zhou Y., Leung H., Yip P., An Exact Maximum Likelihood Registration for Data Fusion, IEEE Trans. Aerospace Electron. System, 1997, Vol.45(6),pp.1560- 1573
[24] Karniely H., Siegelmann H.T., Sensor Registration Using Neural Network, IEEE Aerospace Electron. System, 2000, Vol. 36(1), pp.85-101
[25] Farina A., Conte E., D' Addio E., Longo M., Multistatic Radar Detection: Synthesis and Comparison of Optimum and Suboptimum Receives,IEE Proc. F, 1983, Vol. 133(7), pp. 484 - 494
[26] Hoballah I., Varshney P. K., Distributed Bayesion Signal Detection,IEEE Trans. on IT, 1989, Vol. (35)
[27] Srinvsan R., Sharma P., Malik V., Distributed Detection of Swerling Targets, IEE P. pt. F., 1986
[28] Barkat M., Varshney P. K., Decentralized CFAR Signal Detection, IEEE Trans. Aerospace Electron. System, 1989, Vol.25(2), pp. 141 - 149
[29] Polychronopoulos G., Tsitsiklis N., Explicit Solutions for Some Simple Decentralized Detection Problems, IEEE Trans. Aerospace Electron.System, 1991, Vol.26(2), pp. 282 - 292
[30] Barkaj M., Varshney P. K., Adaptive Cell - averaging CFAR Detection in Distributed Sensor Networks, IEEE Trans. Aerospace Electron. System,1991, Vol.27(3), pp. 424-429
[31] Blum R. S., Kassam A., Optimum Distributed Detection of Weak Signal in Dependent Sensor, IEEE Trans. Aerospace Electron. System, 1992,Vol.38(3), pp. 1066-1079
[32] Tang Z. B., Pattipati K. R., Kleinman D. L., Optimization of Detection Networks: Part II - Tree Strucyures, IEEE Trans. Aerospace Electron. System,1993, Vol. 23(1), pp. 211-221
[33] Gini F., Greco M. V., Lombardini F., Verrazzani L., Distributed CFAR Detection in Weibull Clutter, Proceedings of the 14th GRETSI Symposium on Signal and Image Processing, Juan les Pins, France, 13th-16th September 1993, pp. 1171-1174
[34] Gini F., Lombardini F., Verrazzani L., A Robust Approach to Decentralized CFAR Detection in Spiky Clutter, Proceedings of the EuMC'95 25th European Microwave Conference, Bologna, 4th - 7th September 1995,pp. 584 - 588.
[35] Al - Hussaini E. K., Al - Bassiouni A. M., El - Far Y. A., Decentralized CFAR Signal Detection, Signal Process., 1995, Vol. 44, pp.299 - 307
[36] Lombardini F., Gini F., Verrazzani L., CFAR problems for Distributed Detection in Non -Gaussian Clutter, Proceedings of the 6th international conference on Signal processing applications and technology, Boston,Massachussetts, 1995
[37] Uner M.K., Varshney P.K., Decentralized CFAR Detection in Homogeneous and NonHomogeneous Backgrounds, IEEE Trans. Aerospace Electron.System, 1996, Vol. 32(1), pp. 84 - 97
[38] Blum R. S., Qiao J., Thresholding Optimization for Distributed Order - Statistic CFAR Signal Detection, IEEE Trans. Aerospace Electron. System,1996, Vol. 32(1), pp. 368-377
[39] Blum R.S., Necessary Conditions for Optimum Distributed Sensor Detectors under Neyman - Pearson Criterion, IEEE Transactions on IT, 1996,Vol.42, pp. 990-994
[40] Longo M., Lops M., OS - CFAR Thresholding in Decentralized Radar Systems, IEEE Trans. Aerosp. Electron. Syst., 1996, Vol. 32(4), pp.1257-1267
[41] Amirmehrabi H., Viswanathan R., A New Distributed Constant False Alarm Rate Detector, IEEE Trans. Aerospace Electron. System, 1997,Vol. 33(1), pp. 85-87
[42] Viswanathan R., Varshney P. K., Distributed Detection with Multiple Sensor: Part I - Fundamentals, Proceedings of the IEEE, 1997, Vol. 85(1),pp. 54 - 63
[43] Blum R. S. , Kassan S. A., Distributed Detection with Multiple Sensor:Part II-Advanced Topics, Proceedings of the IEEE, 1997, Vol. 85(1), pp.64-79
[44] Gini F., Lombardini F., Verrazzani L., Decentralized CFAR Detection with Binary integration in Weibull Clutter, IEEE Trans. Aerospace Electron.System, 1997
[45] Gini F., Lombardini F., Verrazzani L., Coverage Area Analysis for Decentralized Detection in Non - Gaussian Clutter, IEEE Trans. Aerospace Electron. System
[46] Gini, F. Lombardini, F. Verrazzani L., Robust Monoparametric Multi - radar CFAR Detection against Non - Gaussian Spiky Clutter, IEE Proc.-Radar, Sonar Navig., 1997, Vol. 144(3), pp. 131 - 140
[47] Gini F., Fabrizo L., Parmod K., On Distributed Signal Detection With Multiple Local Free Parameters, IEEE Trans. Aerospace Electron. System,1999, Vol.35(4), pp.1457 - 1466
[48] Chair Z., Varshney P.K., Optimal Data Fusion in Multiple Sensor Detection Systems, IEEE Trans. Aerospace Electron. System, 1986, pp.98- 101
[49] Thomopoulos S.C.A., Viswanathan R., Bougoulias D.K., Optimal Decision Fusion in Multiple Sensor System, IEEE Trans. Aerospace Electron. System, 1987, Vol.23(5), pp.644 - 653
[50] Thomopulos S.C. A., Viswanathan R., Bougoulias D.K., Optimal distributed decision fusion, IEEE Trans. On Aerospace and Electronic Systems, 1989, 25(5), pp. 761 - 765
[51] Linas J., Data Fusion Technology Forum of C~3MIS, IEEE Conf. 3th on Command, Control, Communication and Management Information System, 1989,pp. 148 - 158
[52] Waltz E., Llinas J., Multisensor Data Fusion, Boston: Artech House,1990
[53] Elias - Fuste A. R., Broquetas - Ibars A., Abtequera J.P., Yuste J. C. M., Data Fusion Center with Inhomogeneous Receiver, IEEE Trans.Aerospace Electron. System, 1992, 28(1), pp.276 - 285
[54] Hall D. H., Mathematical Techniques in Multisensor Data Fusion,Artch House, 1992
[55] Dalle Mese E., Gini F., Greco M.V., Lombardini F., Verrazzani L.,Decision Fusion in a High Resolution Multiradar Network, Proceedings of the NATO AC/243 Panel 3/4 Symposium on Multisensors and Sensor Data Fusion,Brussels, Belgium, 8th - 11th November 1993
[56] Thomopulos S. C. A. and Okello N. N., Design of a robust Multi - radar distributed data fusion system Proceedings of the first, IEEE Regional Conference on Aerospace Contral Systems, May 25 - 27, 1993, West Lake Village, pp. 599-603
[57] Han J., Varshney P.K., Srinivasan R., Distributed Binary Integration, IEEE Trans. Aerospace Electron. System, 1993, Vol.29(1), pp.2-8
[58] Li T., Sethi I.K., Optimal multiple Level Decision Fusion with Distributed Sensors, IEEE Trans. Aerop. Electron. Syst., 1993, Vol.29(4),pp.1252 - 1259
[59] Leung H., Blanchette M., Harrison C.A., A Least squares Fusion of multiple radar data, International Conference on Radar'94, Paris, 1994,pp.364 - 369
[60] Willett P., Alford M., Vannicala V., The Case for Like - sensor Predetection Fusion, IEEE Trans. Aerospace Electron. System, 1994,Vol. 30(4), pp. 986-999
[61] Nickens N., Okello and Stelios E.A., Thompulos, Design of Data Fusion System for Multiradar Target detection, 电子工程信息, 1996, 11
[62] I. Goodman, R. Mahler, and H. Ngueu, Mathematics of Data fusion,Kluwer Academic Publishers 1997
[63] Varshney P.K., Distributed Detection and Data Fusion, New York,Springer - Verlag, 1997
[64] Bowman C. L., Maximun Likelihood Track Correlation for Multisensor Interation, IEEE, 1979, pp. 374 - 376
[65] Escamilla - Ambrosio P.J, Mort N., Multisensor Data Fusion Architecture Based on Adaptive KALMAN Filters and Fuzzy Logic Performance Assessment, ISIF, 2002, pp.1542 - 1549
[66] Willsky A., Combining and Updating of Local Estimation and Regional Maps Along Sets of One - dimensional Tracks, IEEE Trans. on A. C., 1982,Vol. 27(4), pp. 799-812
[67] Chang C.B., Youens L. C., Measurement Correlation for Multiple Sensor Tracking in A Dense Target Environment, IEEE Trans. on A. C., 1982,Vol.27(6), pp. 1250-1252
[68] Kosoka M., A Track Correlation Algorithm for Multisensor Interation,Proceeding of the IEEE/AIAA 5~(th) Digital Avionics Systems Conf., 1983
[69] Farina A., Sergio Pardini, Survey on multiradar tracking systems,Italy, Selenia, 1985, 35-39
[70] Bar - Shalom Y., Campo L., The effect of the Common Process Noise on the Two - sensor Fused Track Covariance, IEEE Trans. Aerospace Electron.System, 1986,22(6), pp. 803 - 805
[71] Sumit R., Ronald A.L., Application of The Block KALMAN Filter to Multisensor Estimation with Uncertain Measurements, IEEE, 1986, pp.2815-2818
[72] Ditzler W. R., A Demonstration of Multisensor Tracking, In Processdings of the 1987 Tri - Service Data Fusion Symposium, 1987, pp. 303-311
[73] Hashemipour H.R., Roy S., Laub A. J., Decentralized Structure for Parallel Kalman Filtering, IEEE Trans. on A. C., 1988, Vol.33(1), pp.88-94
[74] Houles A., Barshalom Y., Multisensor Tracking of a Maneuvering Target in Clutter, IEEE Trans. Aerospace Electron. System, 1989, 25(2),pp. 176-189
[75] Bar - Shalom Y., Multitarget - Multisensor Tracking, Artech House,inc, 1990
[76] Dufour F., Marriton M., Tracking A 3D Maneuvering Target with Passive Sensors, IEEE Trans. Aerospace Electron. System, 1991, 27, pp.725-739
[77] Ronald A. I., Kraig L. A., Multisensor Bearings - Only Tracking Using The MDL Criterion, IEEE Trans. Aerospace Electron. System, 1993, pp.213-217
[78] Ronald A.I., Kraig L. A., A Consistent Estimation Criterion for Multisensor Bearings - Only Tracking, IEEE Trans. Aerospace Electron.System, 1996, Vol. 32(1), pp. 108-119
[79] Ronald A.I., Kraig L.A., A Distributed Bearings - Only Tracking Algorithm Using Reduced Sufficient Statistics, IEEE Trans. Aerospace Electron. System, 1996, Vol. 32(1), pp. 339 - 349
[80] Chang K. C., Saha R. K., Bar - Shalom Y., On Optimal Track - to - Track Fusion, IEEE Trans. Aerospace Electron. System, 1997, 33(4), pp.1271 -1285
[81] Guo Jing, Wang Hao, A class structure design for netted - radar tracking system, Journal of National University of Defense Techology (in Chinese), 1997, 19(2), pp. 78 - 84
[82] Chang K. C., Saha R. K., An Efficient Algorithm for Multisensor Track Fusion, IEEE Trans. Aerospace Electron. System, 1998, 34(1), pp.200-210
[83] Leung Et Al, Evaluation of Multiple Radar Tracking Trackers in Stressful Environments, IEEE Trans. Aerospace Electron. System, 1998,35(4), pp. 664-674
[84] Chan K. C. C., Lee V., Leung H. A., Fuzzy Multisensor Tracking System,SPIE, 1999, 3719(4)
[85] Thomopoulos S. C. A., Okello N.N., Distributed Detection with Consulting Sensor and Communication Cost, IEEE Trans. on Automatic Control.,1992, Vol. 37(9), pp.1398 - 1405
[86] F. Lombardini, Dependent Randomization for Distributed Decision Fusion under Communication Constraints, Proceedings of the IEEE 1996 International Symposium on Information Theory and its Applications,Victoria,B.C.,Canada,17th-20th September 1996,pp.266-269
[87]Lombardini F.,Verrazzani L.,Communication - Constrained Distributed Radar Detection in Spiky Clutter,Proceedings of the IEEE 1996National Radar Conference,Ann Arbor,MI,U.S.A.,13th-16th May 1996,pp.285- 290
[88]阙渭焰,彭应宁,陆大纟金,侯秀英,雷达布站方法,清华大学学报(自然科学版),1997年,Vol.37,pp.45-48
[89]谭贤四,武文,王红,毕红葵,区域警戒雷达网优化部署研究,现代雷达,2001年,第5期,pp.6-10
[90]张吉林,用MATLAB语言实现雷达网的优化部署,现代雷达,2001年,第3期,pp.9-11
[91]王永城,吴小飞,雷达组网反隐身数据融合技术探讨,现代雷达,1994年,第4期,pp.30-36
[92]方振和,刘喜斌,郑德胜,多传感器数据融合技术对抗隐身目标,上海大学学报(自然科学版),1998年,Vol.4(1),pp.107-112
[93]戴筠,雷达组网反隐身技术可行性探讨,现代雷达,1998年,第3期,pp.8-13
[94]伊以新,马晓岩,雷达组网反隐身的优化部署,空军雷达学院报,1999年,(13),pp.25-27
[95]韩英臣,张永顺,刘芸江,雷达组网反隐身技术探讨,电子对抗技术,2003年,Vol.18(2),pp.9-12
[96]花良发,吴铁平,雷达网抗干扰能力度量方法的研究,雷达与对抗,1998年,第4期,pp.4-8
[97]晚安民,陆静,雷达组网的特点及其抗干扰设计,火力与指挥控制,2001年,Vol.26(3),pp.41-44
[98]徐振海,王雪松,肖顺平,庄钊文,雷达组网对抗中遮盖干扰功率优化分配,系统工程与电子技术,2003年,Vol.25(6),pp.655-657
[99]唐雪梅,刘波,推广的多传感器数据融合算法,系统工程与电子技术,1997年,第10期,P:61-66
[100]王永城,吴小飞,组网雷达目标高度算法研究,现代雷达,1998,第3期,pp.14-17
[101]何友,陆大纟金,彭应宁,多级式多传感器信息融合中的状态估计,电子学报,1999年,Vol.27(8),pp.60-63
[102]闵荣宝,惠荣昌,数据融合技术在雷达网中的应用,火控雷达技术,2000,Vol.29(3),pp.17-23
[103]张留山,刘礻韦,灰关联分析法在数据融合中的应用,舰船电子对抗,2000年,第4期,pp.26-28
[104]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(自然科学版),2000年,Vol.27(2),pp.161-165
[105]黄治涛,周一宇,冯道旺,姜文利,基于动态权的决策融合在雷达组网检测低RCS目标中的应用,信号处理,2000年,Vol.16(3),pp.257-261
[106]唐斌兵,组网雷达系统中的数据融合,模糊系统与数学,2001,Vol.15(3),P:105-110
[107]冯富强,尹成友,陈鹏举,一种消除虚假定位目标的方法,现代雷达,2002年,第3期,pp.45-46
[108]卢盈齐,王睿,张平定,组网雷达跟踪系统目标高度测定算法,现代雷达,2002年,第4期,pp.24-26
[109]魏华,李瑞棠,组网雷达中目标照射角的计算及应用,西安电子科技大学学报(自然科学版),2000年,Vol.27(6),pp.696-708
[110]王成,李少洪,黄槐,基于雷达组网的多目标数据融合系统研究,系统工程与电子技术,2002年,Vol.24(7),pp.58-60
[111]赵振山,杨万海,组网雷达定位优化算法,雷达与对抗,2002,第3期,pp.1-4
[112]郭明,许录平,集中式多雷达系统的数据融合,雷达与对抗,2003年,第1期,pp.19-22
[113]徐锦起,李侠,秦江敏,朱剑平,雷达网检测数据的散度判决方法,空军雷达学院院报,2003年,Vol.17(1),pp.25-27
[114]徐锦起,李侠,秦江敏,朱剑平,散度在雷达网目标检测数据融合中的应用,火控雷达技术,2003年,第32卷,pp.53-62
[115]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,1997年,第2期,pp.12-18
[116]徐洪奎,王东进,基于卡尔曼滤波的组网雷达系统目标跟踪分析,系统工程与电子技术,子技术,2001年,Vol.23(11),pp.67-69
[117]宋小全,孙仲康,多传感器机动目标跟踪,电子学报,1997年,Vol.25(9),pp.98-101
[118]何友,陆大纟金,彭应宁,一种多目标多传感器航迹相关算法,清华大学学报(自然科学版),1997年,Vol.37(9),pp.11-19
[119]何友,陆大纟金,彭应宁,高志永,多传感器数据融合系统中两种新的航迹相关算法,电子学报,1997年,Vol.25(9),pp.108-113
[120]叶斌,李瑞棠,李红艳,雷达网目标速度向量测量及其在跟踪中的应用,西安电子科技大学学报,1999年,Vol.26(2),pp.137-140
[121]阙渭焰,一种用于雷达组网反隐身系统的目标跟踪方法,现代雷达,1999年,第2期,pp.28-34
[122]何兵,毛士艺,张有为,李少洪,一种扩展的直接航迹融合方法研究,系统仿真学报,2000年,Vol.12(4),pp.323-326
[123]杨振涛,李思敏,用测角交会法处理多目标跟踪问题,桂林电子工业学院学报,2000年,Vol.20(1),pp.32-36
[124]罗鹏飞,张文明,邹毅智,组网雷达多目标跟踪仿真系统的设计与实现,系统工程与电子技术,2000年,Vol.22(3),pp.33-43
[125]张东洋,任宏滨,简金蕾,雷达最优组网及目标跟踪算法分析,雷达与对抗,2002年,第1期,pp.1-6
[126]郭晶,汪浩,组网雷达跟踪系统的类结构设计,国防科技大学学报,1997年,Vol.19(2),pp.78-84
[127]何兵,毛士艺,张有为,李少洪,不同类型传感器的分层融合算法研究,电子学报,2000年,Vol.28(6),pp.4-7
[128]陆祥君,雷达组网系统误差校正的一种方法,现代雷达,2001年,第6期,P:1-6
[129]李鸿艳,冯新喜,多雷达系统几种误差配准方法的分析与比较,电子对抗技术,2001年,Vol.16(1),pp.37-42
[130]王睿,张平定,刘进忙,雷达组网数据融合系统组合失配误差研究,空军工程大学学报(自然科学版),2002年,Vol.3(1),pp.56-58
[131]赵振山,杨万海,组网雷达对目标三维定位精度仿真分析,电子对抗技术,2003年,Vol.18(1),pp.39-42
[132]严朝译,系统误差校正中的时间对准问题研究,电子对抗技术,2003年,Vol.18(2),pp.13-17
[133]尹以新,何明浩,马晓岩,雷达组网ECCM性能评估方法,系统工程与电子技术,
[134]吴艳,杨万海,李明,分布式多传感器系统的最佳监测和性能,西安电子科技大学学报(自然科学版),2000年,Vol.27(2),P:138-142
[135]花良发,李侠,雷达组网系统性能评估指标分析,雷达与对抗,1999年,第4期,pp.1-6
[136]谭贤四,吴文,王红,毕红葵,区域雷达网分布是检测性能改善的方法,火力与指挥控制,2001年,Vol.26(4),pp.55-58
[137]姜军,王朝墀,李军,反隐身制导雷达组网系统的误差分析与改进途径,战术导弹技术,2002年,第6期,pp.61-65
[138]朱丽莉,王朝墀,制导雷达组网技术研究,现代雷达,2003年,第7期,pp.1-3
[139]陈永光、李修和著,组网雷达作战能力分析与评估,国防工业出版社,2006.年1月
[140]Zhang Y.,Yang X.,Simulation of Air-Defense Radar Network under Saturation Attack,Electrical Engineering,Computer Science and Engineering University of Nebraska,linciln,NE 68588,2002
[141]吴少鹏,华学曾,雷达抗有源压制性干扰性能的指标和评估方法,现代雷达,1999年,第3期,pp.1-6
[142]陈开林,有源干扰机组网技术分析,电子对抗技术,2002年,Vol.16(1),pp.10-13
[143]李军,雷达抗干扰试验中的几个基本问题,舰船电子对抗,1999年,第6期,pp.20-24
[144]焦国娥,反辐射武器对雷达的威胁及对策,舰船电子对抗,1998年,第2期,pp.26-32
[145]谭显裕,对抗低空飞行器威胁的雷达对策及发展趋势,现代防御技术,1996年,第1期,pp.9-20
[146]丁鹭飞,耿富录著,雷达原理,西安电子科技大学出版社,1984年11月
[147]何友、王国宏,陆大纟金、彭应宁著,多传感器信息融合及应用,电子工业出版社,2000年
[148]何友、关键、彭应宁、陆大纟金著,雷达自动检测与恒虚警处理,清华大学出版社,1999年
[149]汪剑鸣 等,浮点遗传算法中的一种新的杂交算子,控制理论与应用,2002年,19(6):977-980
[150]徐毅,金德琨,数据融合体系结构的设计,航空电子技术,2001年12月,第四期,pp.25-31
[151]伊利.布鲁克纳.,雷达技术,国防工业出版社,1986年
[152]周宏仁,敬忠良,王培德,机动目标跟踪,国防工业出版社,1991,8
[153]Kalata P.R.,The Tracking Index:A Generalized Parameter for αβ and αβγ Target Trackers,IEEE Trans.Aerospace Electron.System,1984,20(2),pp.174 - 182
[154]Mazor E.,Averbuch A.,Bar Shalom Y.,Interacting Multiple Model Methods in Target Tracking:a Survey[J],IEEE Trans.Aerospace and Electronic Systems,1998,34(1):103-123
[155]Li Xiao Rong,Bar Shalom Y,Multiple- Model Estimation with Variable Structure[J],IEEE Trans.Aerospace and Electronic Systems,1998,41(4):478 - 493
[156]Durga P.Malladi,Jason L.Speyyer,A New Approach to Multiple Model Adaptive Estimation,Proceedings of the 36th Conference on Decision &Control San Diego,California USA*December 1997,pp.3460- 3467
[157]潘泉,机动目标跟踪双滤波器模型及自适应算法,控制理论与应用,1995,8,12(4),pp482-286
[158]王永生,范洪达,机动目标跟踪的IMM和IMMF-LS自适应滤波算法,计算机仿真,2002,9,19(5),pp48-52
[159]Hurst M P,Mittra.,Scattering Center Analysis Via Prony's Method,IEEE Trans,A.P.35(8):986-988,1987
[160]Carriere R,Moses R L,High Resolution Radar Target Modeling Using a Modified Pronoy Estimator,IEEE Trams A.P.40:13-18,1992
[161]Lih J,yang S H,Using Range Profile as Feature Vector to Identify Aerospace Object,IEEE Trams A.P.41(3):261-268,1993
[162]Liq,Rothwell E J,Chen K M,Nyquist D P,Decttering Center Analysis of Radar Using Fitting Scheme and Genetic Algorithm,IEEE Trans A.P.44(2):198-206,1996
[163]Vignaud L.,Wavelet-RELAX feature extraction in radar images,IEE Proc.Radar Sonar Naving,2003,Vol.150,pp.242- 246
[164]姜卫东,庄钊文,陈增平,基于一距离像的目标识别方法,现代雷达,1999年,第一期,pp.19-22
[165]沈明华,冯新喜 等,遗传算法在飞机目标识别中的应用,系统工程与电子技术,2002年,24(10),pp.28-31
[166]李延军,姜文利,王新政 等,发展中的雷达目标识别,现代雷达,2000年,22(6),pp.1-5
[167]汪洋,陈建文,刘中,导弹目标ISAR称象仿真分析,现代雷达,2003年,20(10),pp.18-21
[168]丁建江,张贤达,低分辨雷达目标智能识别的最新进展,现代雷达,2002年,24(3),pp.1-4
[169]张汉华,王伟,姜卫东,陈增平,庄钊文,基于时间谱信息的低分辨雷达飞机目标的分类,系统工程与电子技术,2004年,26(40),pp.487-490
[170]黄培康,殷红成,许小剑编著,雷达目标特性,电子工业出版社,2005年,3月
[2]陈振邦,低截获概率雷达技术研究,舰船电子对抗,1997年,第1期,pp.7-16
[3]陆林根,多雷达联网目标检测能力研究,系统工程与电子技术,1997年,第10期,pp.37-42
[4]Fanna A.,Studer F.A.,Radar and sensor Netting Present and Future,Microwave Jounal,1998(1),pp.97-100
[5]Sirnvsan R.,Distributed Radar Detection Theory,IEE.P.pt F.,1986,pp.133 - 60
[6]Addio E.D.,Farina A.,Overview of Detection Theory in Multistatic Radar,IEE P pt.F.,1986,pp.133
[7]Hoballah I.,Varshney P.K.,Distributed Bayesion Signal Detection Theory,IEEE Trans.on IT,1989,pp.35
[8]Victor S.C.,Fundamentals of Multisite Radar Systems Multistatic Radars and Multiradar Systems,Gordon and Breach Science Publishers,1998(Originally Published in Russian in 1993)
[9]Deley G.W.,Ballantine J.H.,Tactical Forward Area Surveillance and Control Internetting Study,Final Report,Volume I,Summary,AD-A067935,1978
[10]Blaricum G.F.Van.,Tactical Air Surveillance Radar Nettin(TASRAN)Simulator/Emulator,AD - A110382,1982
[11]Fisher W.L.,Mueche C.E.,Cameron A.G.,Registration Errors in A Netted Surveillance System,MIT Lincoln Laboratory Technical Note,1980-40,Lexington,1980
[12]Grindlay A.,Radar Bias Error Removal Algorithm for A Multiple-Site System,NRL Report 8467,Naval Research Laboratory,Washington,D.C.,1981
[13]Prather D.D.,Estimation of Radar Range Biases at Multiple Site,NRL Report 8510,Naval Research Laboratory,Washington,D.C.,1981
[14]Dana M.P.,Registration:A Prerequisite for Multiple Sensor Tracking In:Multitaget Multisensor Tracking:Advanced Applications, Artech House Inc. 1990
[15] Roy S., Iltis R. A., Decentralized Linear Estimation in Correlated Measurement Noise, IEEE Trans. Aerospace Electron. System, 1991, Vol.27(6),pp. 939-941
[16] Dela Cruz E. J., Alouani A. T., Rice T. R. , Blair W. D., Multisensor Registration, SPIE Conference on Signal and Data Processing of Small Target ' 92, 1992
[17] Dela Cruz E. J., Sensor Registration in Multisensor Systems, Master of Science Thesis, Electrical Engineering Department, Tennessee Technological University, 1992
[18] Dela E. J., Alouani A. T., Rice T. R. , Blair W. D., On Multisensor Track Alignment, IEEE Proceedings of the 32th conference on Decision and Control Tucson, Artzons, 1992
[19] Heliick R. E., Rice A., Removal of Alignment Errors in An Integrated System of Tow 3D Sensors, IEEE Trans. Aerospace Electron. System, 1993,Vol.27(4), pp. 1333-1343
[20] Bas A. , Doom V., Blom A. P., Systematic ERROR Estimation in Multisensor Fusion Systems, SPIE 1954, 1995, pp.450 - 461
[21] Leung H., Gault K., Comparison of Registration Error Correction Techniques for Air Surveillance Radar Network, In: SPIE Proc. Signal and Data Processing of Small Target, San Diego, CA, 1995, pp.498 - 508
[22] Dimitris E. M, Maurice E.C., Effect in Range Difference Position Estimation Due to Stations' Position Error, IEEE Trans. Aerospace Electron.System, 1998, Vol. 34(1), pp. 329 - 334
[23] Zhou Y., Leung H., Yip P., An Exact Maximum Likelihood Registration for Data Fusion, IEEE Trans. Aerospace Electron. System, 1997, Vol.45(6),pp.1560- 1573
[24] Karniely H., Siegelmann H.T., Sensor Registration Using Neural Network, IEEE Aerospace Electron. System, 2000, Vol. 36(1), pp.85-101
[25] Farina A., Conte E., D' Addio E., Longo M., Multistatic Radar Detection: Synthesis and Comparison of Optimum and Suboptimum Receives,IEE Proc. F, 1983, Vol. 133(7), pp. 484 - 494
[26] Hoballah I., Varshney P. K., Distributed Bayesion Signal Detection,IEEE Trans. on IT, 1989, Vol. (35)
[27] Srinvsan R., Sharma P., Malik V., Distributed Detection of Swerling Targets, IEE P. pt. F., 1986
[28] Barkat M., Varshney P. K., Decentralized CFAR Signal Detection, IEEE Trans. Aerospace Electron. System, 1989, Vol.25(2), pp. 141 - 149
[29] Polychronopoulos G., Tsitsiklis N., Explicit Solutions for Some Simple Decentralized Detection Problems, IEEE Trans. Aerospace Electron.System, 1991, Vol.26(2), pp. 282 - 292
[30] Barkaj M., Varshney P. K., Adaptive Cell - averaging CFAR Detection in Distributed Sensor Networks, IEEE Trans. Aerospace Electron. System,1991, Vol.27(3), pp. 424-429
[31] Blum R. S., Kassam A., Optimum Distributed Detection of Weak Signal in Dependent Sensor, IEEE Trans. Aerospace Electron. System, 1992,Vol.38(3), pp. 1066-1079
[32] Tang Z. B., Pattipati K. R., Kleinman D. L., Optimization of Detection Networks: Part II - Tree Strucyures, IEEE Trans. Aerospace Electron. System,1993, Vol. 23(1), pp. 211-221
[33] Gini F., Greco M. V., Lombardini F., Verrazzani L., Distributed CFAR Detection in Weibull Clutter, Proceedings of the 14th GRETSI Symposium on Signal and Image Processing, Juan les Pins, France, 13th-16th September 1993, pp. 1171-1174
[34] Gini F., Lombardini F., Verrazzani L., A Robust Approach to Decentralized CFAR Detection in Spiky Clutter, Proceedings of the EuMC'95 25th European Microwave Conference, Bologna, 4th - 7th September 1995,pp. 584 - 588.
[35] Al - Hussaini E. K., Al - Bassiouni A. M., El - Far Y. A., Decentralized CFAR Signal Detection, Signal Process., 1995, Vol. 44, pp.299 - 307
[36] Lombardini F., Gini F., Verrazzani L., CFAR problems for Distributed Detection in Non -Gaussian Clutter, Proceedings of the 6th international conference on Signal processing applications and technology, Boston,Massachussetts, 1995
[37] Uner M.K., Varshney P.K., Decentralized CFAR Detection in Homogeneous and NonHomogeneous Backgrounds, IEEE Trans. Aerospace Electron.System, 1996, Vol. 32(1), pp. 84 - 97
[38] Blum R. S., Qiao J., Thresholding Optimization for Distributed Order - Statistic CFAR Signal Detection, IEEE Trans. Aerospace Electron. System,1996, Vol. 32(1), pp. 368-377
[39] Blum R.S., Necessary Conditions for Optimum Distributed Sensor Detectors under Neyman - Pearson Criterion, IEEE Transactions on IT, 1996,Vol.42, pp. 990-994
[40] Longo M., Lops M., OS - CFAR Thresholding in Decentralized Radar Systems, IEEE Trans. Aerosp. Electron. Syst., 1996, Vol. 32(4), pp.1257-1267
[41] Amirmehrabi H., Viswanathan R., A New Distributed Constant False Alarm Rate Detector, IEEE Trans. Aerospace Electron. System, 1997,Vol. 33(1), pp. 85-87
[42] Viswanathan R., Varshney P. K., Distributed Detection with Multiple Sensor: Part I - Fundamentals, Proceedings of the IEEE, 1997, Vol. 85(1),pp. 54 - 63
[43] Blum R. S. , Kassan S. A., Distributed Detection with Multiple Sensor:Part II-Advanced Topics, Proceedings of the IEEE, 1997, Vol. 85(1), pp.64-79
[44] Gini F., Lombardini F., Verrazzani L., Decentralized CFAR Detection with Binary integration in Weibull Clutter, IEEE Trans. Aerospace Electron.System, 1997
[45] Gini F., Lombardini F., Verrazzani L., Coverage Area Analysis for Decentralized Detection in Non - Gaussian Clutter, IEEE Trans. Aerospace Electron. System
[46] Gini, F. Lombardini, F. Verrazzani L., Robust Monoparametric Multi - radar CFAR Detection against Non - Gaussian Spiky Clutter, IEE Proc.-Radar, Sonar Navig., 1997, Vol. 144(3), pp. 131 - 140
[47] Gini F., Fabrizo L., Parmod K., On Distributed Signal Detection With Multiple Local Free Parameters, IEEE Trans. Aerospace Electron. System,1999, Vol.35(4), pp.1457 - 1466
[48] Chair Z., Varshney P.K., Optimal Data Fusion in Multiple Sensor Detection Systems, IEEE Trans. Aerospace Electron. System, 1986, pp.98- 101
[49] Thomopoulos S.C.A., Viswanathan R., Bougoulias D.K., Optimal Decision Fusion in Multiple Sensor System, IEEE Trans. Aerospace Electron. System, 1987, Vol.23(5), pp.644 - 653
[50] Thomopulos S.C. A., Viswanathan R., Bougoulias D.K., Optimal distributed decision fusion, IEEE Trans. On Aerospace and Electronic Systems, 1989, 25(5), pp. 761 - 765
[51] Linas J., Data Fusion Technology Forum of C~3MIS, IEEE Conf. 3th on Command, Control, Communication and Management Information System, 1989,pp. 148 - 158
[52] Waltz E., Llinas J., Multisensor Data Fusion, Boston: Artech House,1990
[53] Elias - Fuste A. R., Broquetas - Ibars A., Abtequera J.P., Yuste J. C. M., Data Fusion Center with Inhomogeneous Receiver, IEEE Trans.Aerospace Electron. System, 1992, 28(1), pp.276 - 285
[54] Hall D. H., Mathematical Techniques in Multisensor Data Fusion,Artch House, 1992
[55] Dalle Mese E., Gini F., Greco M.V., Lombardini F., Verrazzani L.,Decision Fusion in a High Resolution Multiradar Network, Proceedings of the NATO AC/243 Panel 3/4 Symposium on Multisensors and Sensor Data Fusion,Brussels, Belgium, 8th - 11th November 1993
[56] Thomopulos S. C. A. and Okello N. N., Design of a robust Multi - radar distributed data fusion system Proceedings of the first, IEEE Regional Conference on Aerospace Contral Systems, May 25 - 27, 1993, West Lake Village, pp. 599-603
[57] Han J., Varshney P.K., Srinivasan R., Distributed Binary Integration, IEEE Trans. Aerospace Electron. System, 1993, Vol.29(1), pp.2-8
[58] Li T., Sethi I.K., Optimal multiple Level Decision Fusion with Distributed Sensors, IEEE Trans. Aerop. Electron. Syst., 1993, Vol.29(4),pp.1252 - 1259
[59] Leung H., Blanchette M., Harrison C.A., A Least squares Fusion of multiple radar data, International Conference on Radar'94, Paris, 1994,pp.364 - 369
[60] Willett P., Alford M., Vannicala V., The Case for Like - sensor Predetection Fusion, IEEE Trans. Aerospace Electron. System, 1994,Vol. 30(4), pp. 986-999
[61] Nickens N., Okello and Stelios E.A., Thompulos, Design of Data Fusion System for Multiradar Target detection, 电子工程信息, 1996, 11
[62] I. Goodman, R. Mahler, and H. Ngueu, Mathematics of Data fusion,Kluwer Academic Publishers 1997
[63] Varshney P.K., Distributed Detection and Data Fusion, New York,Springer - Verlag, 1997
[64] Bowman C. L., Maximun Likelihood Track Correlation for Multisensor Interation, IEEE, 1979, pp. 374 - 376
[65] Escamilla - Ambrosio P.J, Mort N., Multisensor Data Fusion Architecture Based on Adaptive KALMAN Filters and Fuzzy Logic Performance Assessment, ISIF, 2002, pp.1542 - 1549
[66] Willsky A., Combining and Updating of Local Estimation and Regional Maps Along Sets of One - dimensional Tracks, IEEE Trans. on A. C., 1982,Vol. 27(4), pp. 799-812
[67] Chang C.B., Youens L. C., Measurement Correlation for Multiple Sensor Tracking in A Dense Target Environment, IEEE Trans. on A. C., 1982,Vol.27(6), pp. 1250-1252
[68] Kosoka M., A Track Correlation Algorithm for Multisensor Interation,Proceeding of the IEEE/AIAA 5~(th) Digital Avionics Systems Conf., 1983
[69] Farina A., Sergio Pardini, Survey on multiradar tracking systems,Italy, Selenia, 1985, 35-39
[70] Bar - Shalom Y., Campo L., The effect of the Common Process Noise on the Two - sensor Fused Track Covariance, IEEE Trans. Aerospace Electron.System, 1986,22(6), pp. 803 - 805
[71] Sumit R., Ronald A.L., Application of The Block KALMAN Filter to Multisensor Estimation with Uncertain Measurements, IEEE, 1986, pp.2815-2818
[72] Ditzler W. R., A Demonstration of Multisensor Tracking, In Processdings of the 1987 Tri - Service Data Fusion Symposium, 1987, pp. 303-311
[73] Hashemipour H.R., Roy S., Laub A. J., Decentralized Structure for Parallel Kalman Filtering, IEEE Trans. on A. C., 1988, Vol.33(1), pp.88-94
[74] Houles A., Barshalom Y., Multisensor Tracking of a Maneuvering Target in Clutter, IEEE Trans. Aerospace Electron. System, 1989, 25(2),pp. 176-189
[75] Bar - Shalom Y., Multitarget - Multisensor Tracking, Artech House,inc, 1990
[76] Dufour F., Marriton M., Tracking A 3D Maneuvering Target with Passive Sensors, IEEE Trans. Aerospace Electron. System, 1991, 27, pp.725-739
[77] Ronald A. I., Kraig L. A., Multisensor Bearings - Only Tracking Using The MDL Criterion, IEEE Trans. Aerospace Electron. System, 1993, pp.213-217
[78] Ronald A.I., Kraig L. A., A Consistent Estimation Criterion for Multisensor Bearings - Only Tracking, IEEE Trans. Aerospace Electron.System, 1996, Vol. 32(1), pp. 108-119
[79] Ronald A.I., Kraig L.A., A Distributed Bearings - Only Tracking Algorithm Using Reduced Sufficient Statistics, IEEE Trans. Aerospace Electron. System, 1996, Vol. 32(1), pp. 339 - 349
[80] Chang K. C., Saha R. K., Bar - Shalom Y., On Optimal Track - to - Track Fusion, IEEE Trans. Aerospace Electron. System, 1997, 33(4), pp.1271 -1285
[81] Guo Jing, Wang Hao, A class structure design for netted - radar tracking system, Journal of National University of Defense Techology (in Chinese), 1997, 19(2), pp. 78 - 84
[82] Chang K. C., Saha R. K., An Efficient Algorithm for Multisensor Track Fusion, IEEE Trans. Aerospace Electron. System, 1998, 34(1), pp.200-210
[83] Leung Et Al, Evaluation of Multiple Radar Tracking Trackers in Stressful Environments, IEEE Trans. Aerospace Electron. System, 1998,35(4), pp. 664-674
[84] Chan K. C. C., Lee V., Leung H. A., Fuzzy Multisensor Tracking System,SPIE, 1999, 3719(4)
[85] Thomopoulos S. C. A., Okello N.N., Distributed Detection with Consulting Sensor and Communication Cost, IEEE Trans. on Automatic Control.,1992, Vol. 37(9), pp.1398 - 1405
[86] F. Lombardini, Dependent Randomization for Distributed Decision Fusion under Communication Constraints, Proceedings of the IEEE 1996 International Symposium on Information Theory and its Applications,Victoria,B.C.,Canada,17th-20th September 1996,pp.266-269
[87]Lombardini F.,Verrazzani L.,Communication - Constrained Distributed Radar Detection in Spiky Clutter,Proceedings of the IEEE 1996National Radar Conference,Ann Arbor,MI,U.S.A.,13th-16th May 1996,pp.285- 290
[88]阙渭焰,彭应宁,陆大纟金,侯秀英,雷达布站方法,清华大学学报(自然科学版),1997年,Vol.37,pp.45-48
[89]谭贤四,武文,王红,毕红葵,区域警戒雷达网优化部署研究,现代雷达,2001年,第5期,pp.6-10
[90]张吉林,用MATLAB语言实现雷达网的优化部署,现代雷达,2001年,第3期,pp.9-11
[91]王永城,吴小飞,雷达组网反隐身数据融合技术探讨,现代雷达,1994年,第4期,pp.30-36
[92]方振和,刘喜斌,郑德胜,多传感器数据融合技术对抗隐身目标,上海大学学报(自然科学版),1998年,Vol.4(1),pp.107-112
[93]戴筠,雷达组网反隐身技术可行性探讨,现代雷达,1998年,第3期,pp.8-13
[94]伊以新,马晓岩,雷达组网反隐身的优化部署,空军雷达学院报,1999年,(13),pp.25-27
[95]韩英臣,张永顺,刘芸江,雷达组网反隐身技术探讨,电子对抗技术,2003年,Vol.18(2),pp.9-12
[96]花良发,吴铁平,雷达网抗干扰能力度量方法的研究,雷达与对抗,1998年,第4期,pp.4-8
[97]晚安民,陆静,雷达组网的特点及其抗干扰设计,火力与指挥控制,2001年,Vol.26(3),pp.41-44
[98]徐振海,王雪松,肖顺平,庄钊文,雷达组网对抗中遮盖干扰功率优化分配,系统工程与电子技术,2003年,Vol.25(6),pp.655-657
[99]唐雪梅,刘波,推广的多传感器数据融合算法,系统工程与电子技术,1997年,第10期,P:61-66
[100]王永城,吴小飞,组网雷达目标高度算法研究,现代雷达,1998,第3期,pp.14-17
[101]何友,陆大纟金,彭应宁,多级式多传感器信息融合中的状态估计,电子学报,1999年,Vol.27(8),pp.60-63
[102]闵荣宝,惠荣昌,数据融合技术在雷达网中的应用,火控雷达技术,2000,Vol.29(3),pp.17-23
[103]张留山,刘礻韦,灰关联分析法在数据融合中的应用,舰船电子对抗,2000年,第4期,pp.26-28
[104]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(自然科学版),2000年,Vol.27(2),pp.161-165
[105]黄治涛,周一宇,冯道旺,姜文利,基于动态权的决策融合在雷达组网检测低RCS目标中的应用,信号处理,2000年,Vol.16(3),pp.257-261
[106]唐斌兵,组网雷达系统中的数据融合,模糊系统与数学,2001,Vol.15(3),P:105-110
[107]冯富强,尹成友,陈鹏举,一种消除虚假定位目标的方法,现代雷达,2002年,第3期,pp.45-46
[108]卢盈齐,王睿,张平定,组网雷达跟踪系统目标高度测定算法,现代雷达,2002年,第4期,pp.24-26
[109]魏华,李瑞棠,组网雷达中目标照射角的计算及应用,西安电子科技大学学报(自然科学版),2000年,Vol.27(6),pp.696-708
[110]王成,李少洪,黄槐,基于雷达组网的多目标数据融合系统研究,系统工程与电子技术,2002年,Vol.24(7),pp.58-60
[111]赵振山,杨万海,组网雷达定位优化算法,雷达与对抗,2002,第3期,pp.1-4
[112]郭明,许录平,集中式多雷达系统的数据融合,雷达与对抗,2003年,第1期,pp.19-22
[113]徐锦起,李侠,秦江敏,朱剑平,雷达网检测数据的散度判决方法,空军雷达学院院报,2003年,Vol.17(1),pp.25-27
[114]徐锦起,李侠,秦江敏,朱剑平,散度在雷达网目标检测数据融合中的应用,火控雷达技术,2003年,第32卷,pp.53-62
[115]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,1997年,第2期,pp.12-18
[116]徐洪奎,王东进,基于卡尔曼滤波的组网雷达系统目标跟踪分析,系统工程与电子技术,子技术,2001年,Vol.23(11),pp.67-69
[117]宋小全,孙仲康,多传感器机动目标跟踪,电子学报,1997年,Vol.25(9),pp.98-101
[118]何友,陆大纟金,彭应宁,一种多目标多传感器航迹相关算法,清华大学学报(自然科学版),1997年,Vol.37(9),pp.11-19
[119]何友,陆大纟金,彭应宁,高志永,多传感器数据融合系统中两种新的航迹相关算法,电子学报,1997年,Vol.25(9),pp.108-113
[120]叶斌,李瑞棠,李红艳,雷达网目标速度向量测量及其在跟踪中的应用,西安电子科技大学学报,1999年,Vol.26(2),pp.137-140
[121]阙渭焰,一种用于雷达组网反隐身系统的目标跟踪方法,现代雷达,1999年,第2期,pp.28-34
[122]何兵,毛士艺,张有为,李少洪,一种扩展的直接航迹融合方法研究,系统仿真学报,2000年,Vol.12(4),pp.323-326
[123]杨振涛,李思敏,用测角交会法处理多目标跟踪问题,桂林电子工业学院学报,2000年,Vol.20(1),pp.32-36
[124]罗鹏飞,张文明,邹毅智,组网雷达多目标跟踪仿真系统的设计与实现,系统工程与电子技术,2000年,Vol.22(3),pp.33-43
[125]张东洋,任宏滨,简金蕾,雷达最优组网及目标跟踪算法分析,雷达与对抗,2002年,第1期,pp.1-6
[126]郭晶,汪浩,组网雷达跟踪系统的类结构设计,国防科技大学学报,1997年,Vol.19(2),pp.78-84
[127]何兵,毛士艺,张有为,李少洪,不同类型传感器的分层融合算法研究,电子学报,2000年,Vol.28(6),pp.4-7
[128]陆祥君,雷达组网系统误差校正的一种方法,现代雷达,2001年,第6期,P:1-6
[129]李鸿艳,冯新喜,多雷达系统几种误差配准方法的分析与比较,电子对抗技术,2001年,Vol.16(1),pp.37-42
[130]王睿,张平定,刘进忙,雷达组网数据融合系统组合失配误差研究,空军工程大学学报(自然科学版),2002年,Vol.3(1),pp.56-58
[131]赵振山,杨万海,组网雷达对目标三维定位精度仿真分析,电子对抗技术,2003年,Vol.18(1),pp.39-42
[132]严朝译,系统误差校正中的时间对准问题研究,电子对抗技术,2003年,Vol.18(2),pp.13-17
[133]尹以新,何明浩,马晓岩,雷达组网ECCM性能评估方法,系统工程与电子技术,
[134]吴艳,杨万海,李明,分布式多传感器系统的最佳监测和性能,西安电子科技大学学报(自然科学版),2000年,Vol.27(2),P:138-142
[135]花良发,李侠,雷达组网系统性能评估指标分析,雷达与对抗,1999年,第4期,pp.1-6
[136]谭贤四,吴文,王红,毕红葵,区域雷达网分布是检测性能改善的方法,火力与指挥控制,2001年,Vol.26(4),pp.55-58
[137]姜军,王朝墀,李军,反隐身制导雷达组网系统的误差分析与改进途径,战术导弹技术,2002年,第6期,pp.61-65
[138]朱丽莉,王朝墀,制导雷达组网技术研究,现代雷达,2003年,第7期,pp.1-3
[139]陈永光、李修和著,组网雷达作战能力分析与评估,国防工业出版社,2006.年1月
[140]Zhang Y.,Yang X.,Simulation of Air-Defense Radar Network under Saturation Attack,Electrical Engineering,Computer Science and Engineering University of Nebraska,linciln,NE 68588,2002
[141]吴少鹏,华学曾,雷达抗有源压制性干扰性能的指标和评估方法,现代雷达,1999年,第3期,pp.1-6
[142]陈开林,有源干扰机组网技术分析,电子对抗技术,2002年,Vol.16(1),pp.10-13
[143]李军,雷达抗干扰试验中的几个基本问题,舰船电子对抗,1999年,第6期,pp.20-24
[144]焦国娥,反辐射武器对雷达的威胁及对策,舰船电子对抗,1998年,第2期,pp.26-32
[145]谭显裕,对抗低空飞行器威胁的雷达对策及发展趋势,现代防御技术,1996年,第1期,pp.9-20
[146]丁鹭飞,耿富录著,雷达原理,西安电子科技大学出版社,1984年11月
[147]何友、王国宏,陆大纟金、彭应宁著,多传感器信息融合及应用,电子工业出版社,2000年
[148]何友、关键、彭应宁、陆大纟金著,雷达自动检测与恒虚警处理,清华大学出版社,1999年
[149]汪剑鸣 等,浮点遗传算法中的一种新的杂交算子,控制理论与应用,2002年,19(6):977-980
[150]徐毅,金德琨,数据融合体系结构的设计,航空电子技术,2001年12月,第四期,pp.25-31
[151]伊利.布鲁克纳.,雷达技术,国防工业出版社,1986年
[152]周宏仁,敬忠良,王培德,机动目标跟踪,国防工业出版社,1991,8
[153]Kalata P.R.,The Tracking Index:A Generalized Parameter for αβ and αβγ Target Trackers,IEEE Trans.Aerospace Electron.System,1984,20(2),pp.174 - 182
[154]Mazor E.,Averbuch A.,Bar Shalom Y.,Interacting Multiple Model Methods in Target Tracking:a Survey[J],IEEE Trans.Aerospace and Electronic Systems,1998,34(1):103-123
[155]Li Xiao Rong,Bar Shalom Y,Multiple- Model Estimation with Variable Structure[J],IEEE Trans.Aerospace and Electronic Systems,1998,41(4):478 - 493
[156]Durga P.Malladi,Jason L.Speyyer,A New Approach to Multiple Model Adaptive Estimation,Proceedings of the 36th Conference on Decision &Control San Diego,California USA*December 1997,pp.3460- 3467
[157]潘泉,机动目标跟踪双滤波器模型及自适应算法,控制理论与应用,1995,8,12(4),pp482-286
[158]王永生,范洪达,机动目标跟踪的IMM和IMMF-LS自适应滤波算法,计算机仿真,2002,9,19(5),pp48-52
[159]Hurst M P,Mittra.,Scattering Center Analysis Via Prony's Method,IEEE Trans,A.P.35(8):986-988,1987
[160]Carriere R,Moses R L,High Resolution Radar Target Modeling Using a Modified Pronoy Estimator,IEEE Trams A.P.40:13-18,1992
[161]Lih J,yang S H,Using Range Profile as Feature Vector to Identify Aerospace Object,IEEE Trams A.P.41(3):261-268,1993
[162]Liq,Rothwell E J,Chen K M,Nyquist D P,Decttering Center Analysis of Radar Using Fitting Scheme and Genetic Algorithm,IEEE Trans A.P.44(2):198-206,1996
[163]Vignaud L.,Wavelet-RELAX feature extraction in radar images,IEE Proc.Radar Sonar Naving,2003,Vol.150,pp.242- 246
[164]姜卫东,庄钊文,陈增平,基于一距离像的目标识别方法,现代雷达,1999年,第一期,pp.19-22
[165]沈明华,冯新喜 等,遗传算法在飞机目标识别中的应用,系统工程与电子技术,2002年,24(10),pp.28-31
[166]李延军,姜文利,王新政 等,发展中的雷达目标识别,现代雷达,2000年,22(6),pp.1-5
[167]汪洋,陈建文,刘中,导弹目标ISAR称象仿真分析,现代雷达,2003年,20(10),pp.18-21
[168]丁建江,张贤达,低分辨雷达目标智能识别的最新进展,现代雷达,2002年,24(3),pp.1-4
[169]张汉华,王伟,姜卫东,陈增平,庄钊文,基于时间谱信息的低分辨雷达飞机目标的分类,系统工程与电子技术,2004年,26(40),pp.487-490
[170]黄培康,殷红成,许小剑编著,雷达目标特性,电子工业出版社,2005年,3月