外辐射源雷达目标定位与跟踪方法研究
|
摘要
外辐射源雷达又称无源相干定位雷达(passive coherent location radar，PCL)，是一种新体制雷达。与传统雷达不同，外辐射源雷达自身并不向外辐射电磁波能量，而是将第三方辐射源（如调频广播、电视台、手机GSM基站和GPS卫星等）信号作为照射源，仅靠被动接收目标的反射信号来实现对目标的探测。外辐射源雷达凭借被动探测的特点，使其不易被敌方发现，可免遭反辐射导弹的攻击，具有很强的抗干扰和生存能力；此外，通过借助广播、电视和通信等信号的信号特性，外辐射源雷达还具有较好的低空探测和反隐身性能。因此，在现如今雷达外部电磁环境急剧恶化的情况下，外辐射源雷达成为有效探测目标的一种重要途径并具有重要的研究价值。
本文对外辐射源雷达定位跟踪中的若干具体问题，如定位精度、收发站不同配置对定位与跟踪的影响、闪烁噪声对跟踪的影响、非线性和非高斯滤波问题、机动目标跟踪等方面进行了深入的研究。论文内容可概括为如下六部分：
第一部分，介绍了外辐射源雷达的概念、产生背景及研究价值，回顾了外辐射源雷达的发展历史及外辐射源雷达跟踪的研究现状。简介了该雷达的信号处理流程，并指出其关键技术，简述了外辐射源雷达的定位原理与方法。基于贝叶斯理论给出了经典卡尔曼滤波和扩展卡尔曼滤波（EKF）的推导过程。指出目前外辐射源雷达定位与跟踪中存在的难题，提出基于粒子滤波的外辐射源雷达目标跟踪思路，为后续研究奠定基础。
第二部分，研究了外辐射源雷达的目标定位精度问题。首先对外辐射源雷达目标参数的测量精度进行分析，指出影响参数测量精度的因素；然后推导了基于不同定位方法的目标定位精度几何稀释（GDOP）的计算公式，利用GDOP分析了不同定位方法、不同定位体制以及不同雷达站布局对目标定位精度的影响情况。指出适合外辐射源雷达的目标定位方法与定位体制。
第三部分，研究了外辐射源雷达在非高斯（闪烁）噪声环境下的目标跟踪方法。针对EKF算法的跟踪精度受闪烁噪声影响较大的问题，结合到达时间（TOA）定位技术和粒子滤波，提出一种适于闪烁噪声环境的外辐射源雷达目标跟踪方法。该方法通过多站TOA获得测量信息，利用高斯分布噪声和t分布噪声构建了非高斯分布噪声，采用非线性和非高斯的粒子滤波进行跟踪，避免EKF算法因线性化而带来的误差。实验表明该方法的跟踪性能优于EKF，尤其受闪烁噪声影响小。实测数据进一步验证了该方法的有效性。
第四部分，研究了基于差分演化的粒子滤波跟踪算法。针对粒子滤波算法中传统重采样方法导致的粒子贫化现象，将种群优化的思想引入到粒子滤波的重采样过程中，把重采样过程看作是寻找最优样本点的过程，提出一种基于差分演化算法的重采样方法，利用提出的重采样方法构建了差分演化粒子滤波算法，并将其应用到外辐射源雷达跟踪中。通过计算机仿真对比了新算法和粒子滤波算法的性能，并分析了影响新算法性能的因素。结果表明新算法具有更高的估计精度和运算效率。
第五部分，研究了改进的差分演化粒子滤波跟踪算法。首先为了能从更加逼近真实状态的分布函数中采样，并充分利用最新的观测信息，采用UKF来产生粒子滤波的重要性分布函数。另外，在差分演化重采样方法基础上，对差分演化算法在差分变异操作进行改进，提出四种改进的差分演化重采样方法。然后，基于UKF和改进的差分演化重采样方法，提出了四种改进的差分演化粒子滤波跟踪算法。仿真实验证明，所提四种改进算法在估计性能上优于差分演化粒子滤波和无迹粒子滤波（UPF）。
第六部分，研究了闪烁噪声环境下的外辐射源雷达机动目标跟踪算法。建立了基于多组TOA测量的外辐射源雷达机动目标运动模型。为了解决闪烁噪声环境下的机动目标跟踪问题，以IMM算法为框架并将其与粒子滤波和TOA定位方法相结合，提出一种适用于闪烁噪声环境下的外辐射源雷达机动目标跟踪算法。仿真分析了闪烁噪声和收发站布局对机动目标跟踪的影响，并证明所提方法跟踪精度较高，且受闪烁噪声和收发站布局的影响较小。
As a new type of radar, external illuminator based passive radar (also namedpassive coherent location radar) is a variation of bistatic radar that exploitsnon-cooperative illuminators of opportunity (such as FM broadcast, television, GSM,and GPS transmitters, etc) as its radar transmitters, and it is to detect target by onlyreceiving the echo signal reflected by the target. Unlike the traditional radar, its receiveris absolute passive, making it far less vulnerable to electronic counter measures andanti-radiation guided missile, so this radar may have better survival in a hostileenvironment. In addition, PCL radar has better ability to detect low altitude target andstealth target by right of the signal characteristic of FM, television, etc. Therefore, PCLradar has become one of the most effective approaches to detect target undercomplicated electromagnetism environment, and it is valuable to study.
This dissertation addresses some aspects of PCL Radar for target localization andtracking. The research focus on the target localization precision, the influence of theconfiguration of PCL radar on target localization and tracking, the influence of glintnoise on target tracking, nonlinear and non-Gaussian filtering problem, andmaneuvering target tracking.
The main content of this dissertation is summarized as follows.
In the first part, the concept, the background, and the study value of PCL radar areintroduced, an overview of the development history and the available tracking methodsfor PCL radar is given. Then, we brief introduce the signal processing flow, the keytechnologies of the PCL radar, and the basic location methods. We also derive theKalman filter and the Extended Kalman filter based on the Bayesian theorem, and pointout the puzzle in PCL radar localization and tracking. At last, the particle filter-basedidea for target tracking of PCL radar is proposed, which form the basis of the followingstudy.
In the second part, the accuracy of target localization in PCL radar is studied. First,the precision of measurements parameter is analyzed, and the factors which affect theprecision of measurements parameter are pointed out. Then, geometrical dilution ofprecision (GDOP) of the target relative coordinate errors are formulated. The influenceof the target location method, the mode of location, and the configuration of PCL radaron the GDOP is analyzed. Finally, the location method and the mode of location whichsuit for PCL radar are found out.
In the third part, the tracking algorithm for PCL radar in the presence of non-Gaussian noise (glint noise) is studied. In PCL radar, the tracking performance ofEKF is affected seriously by the glint noise. To solve this problem, a new passive radartracking method is proposed based on particle filter (PF) and time of arrival (TOA)measurements. The method gains TOA measurements by multi-stations, utilizes aGaussian distribution and a student distribution to construct the glint distribution andthen uses nonlinear and non-Gaussian PF to track target, which avoids the error causedby EKF linearization. The simulations show that the new method overcomes the EKF,especially in glint noise environment. The real data farther demonstrates the validity ofthe proposed method.
In the fourth part, an improved particle filtering based on differential evolution(DE) algorithm is proposed. The traditional resampling scheme results in theimpoverishment phenomenon. To deal with this problem, the evolution idea isintroduced to the resampling scheme in particle filtering, and a new resampling schemebased on the DE algorithm is presented, in which the resampling process is regarded asa process for searching better particles. By using this scheme, an differential evolutionparticle filter is proposed, and then it is applied to the target tracking in PCL radar. Theperformance of the new PF and the standard PF is analyzed by the simulations, and thefactors affect the new PF are also analyzed. Simulation results demonstrate that theproposed PF outperforms the standard PF
In the fifth part, the differential evolution particle filter is further studied. Anunscented Kalman filter (UKF) is used to generate the importance proposal distribution(IPD) of particle filter, which matches the true posterior more closely and canincorporate the latest observations into a prior updating routine. In addition, fourdifferential aberrance operators are used in the DE resampling scheme, and four DEresampling schemes are presented. As a result, four types of differential evolutionparticle filters (DEPFs) are proposed, in which the UKF is utilised to generate the IPDand the DE resampling schemes are used as the resampling scheme. Simulation resultsdemonstrate that the proposed DEPFs outperform the basic differential evolutionparticle filter, and the unscented particle filter.
In the sixth part, the problem of manoeuvring target tracking in PCL radar withglint noise is studied. The dynamic state space (DSS) model of manoeuvring target isconstructed. To deal with the problem of manoeuvring target tracking in the presence ofglint noise, an interacting multiple model (IMM) particle filtering method usingmultiple TOA measurements from several transmitter-receiver pairs is proposed andevaluated. The influence of glint noise and configuration of transmitters and receiver stations on the tracking method are analyzed. Simulations illustrate that, compared tothe IMM and the standard PF, the proposed method obtains better estimates of position,velocity, and acceleration, and is smaller affected by the glint noise and configuration ofthe PCL radar.
本文对外辐射源雷达定位跟踪中的若干具体问题，如定位精度、收发站不同配置对定位与跟踪的影响、闪烁噪声对跟踪的影响、非线性和非高斯滤波问题、机动目标跟踪等方面进行了深入的研究。论文内容可概括为如下六部分：
第一部分，介绍了外辐射源雷达的概念、产生背景及研究价值，回顾了外辐射源雷达的发展历史及外辐射源雷达跟踪的研究现状。简介了该雷达的信号处理流程，并指出其关键技术，简述了外辐射源雷达的定位原理与方法。基于贝叶斯理论给出了经典卡尔曼滤波和扩展卡尔曼滤波（EKF）的推导过程。指出目前外辐射源雷达定位与跟踪中存在的难题，提出基于粒子滤波的外辐射源雷达目标跟踪思路，为后续研究奠定基础。
第二部分，研究了外辐射源雷达的目标定位精度问题。首先对外辐射源雷达目标参数的测量精度进行分析，指出影响参数测量精度的因素；然后推导了基于不同定位方法的目标定位精度几何稀释（GDOP）的计算公式，利用GDOP分析了不同定位方法、不同定位体制以及不同雷达站布局对目标定位精度的影响情况。指出适合外辐射源雷达的目标定位方法与定位体制。
第三部分，研究了外辐射源雷达在非高斯（闪烁）噪声环境下的目标跟踪方法。针对EKF算法的跟踪精度受闪烁噪声影响较大的问题，结合到达时间（TOA）定位技术和粒子滤波，提出一种适于闪烁噪声环境的外辐射源雷达目标跟踪方法。该方法通过多站TOA获得测量信息，利用高斯分布噪声和t分布噪声构建了非高斯分布噪声，采用非线性和非高斯的粒子滤波进行跟踪，避免EKF算法因线性化而带来的误差。实验表明该方法的跟踪性能优于EKF，尤其受闪烁噪声影响小。实测数据进一步验证了该方法的有效性。
第四部分，研究了基于差分演化的粒子滤波跟踪算法。针对粒子滤波算法中传统重采样方法导致的粒子贫化现象，将种群优化的思想引入到粒子滤波的重采样过程中，把重采样过程看作是寻找最优样本点的过程，提出一种基于差分演化算法的重采样方法，利用提出的重采样方法构建了差分演化粒子滤波算法，并将其应用到外辐射源雷达跟踪中。通过计算机仿真对比了新算法和粒子滤波算法的性能，并分析了影响新算法性能的因素。结果表明新算法具有更高的估计精度和运算效率。
第五部分，研究了改进的差分演化粒子滤波跟踪算法。首先为了能从更加逼近真实状态的分布函数中采样，并充分利用最新的观测信息，采用UKF来产生粒子滤波的重要性分布函数。另外，在差分演化重采样方法基础上，对差分演化算法在差分变异操作进行改进，提出四种改进的差分演化重采样方法。然后，基于UKF和改进的差分演化重采样方法，提出了四种改进的差分演化粒子滤波跟踪算法。仿真实验证明，所提四种改进算法在估计性能上优于差分演化粒子滤波和无迹粒子滤波（UPF）。
第六部分，研究了闪烁噪声环境下的外辐射源雷达机动目标跟踪算法。建立了基于多组TOA测量的外辐射源雷达机动目标运动模型。为了解决闪烁噪声环境下的机动目标跟踪问题，以IMM算法为框架并将其与粒子滤波和TOA定位方法相结合，提出一种适用于闪烁噪声环境下的外辐射源雷达机动目标跟踪算法。仿真分析了闪烁噪声和收发站布局对机动目标跟踪的影响，并证明所提方法跟踪精度较高，且受闪烁噪声和收发站布局的影响较小。
As a new type of radar, external illuminator based passive radar (also namedpassive coherent location radar) is a variation of bistatic radar that exploitsnon-cooperative illuminators of opportunity (such as FM broadcast, television, GSM,and GPS transmitters, etc) as its radar transmitters, and it is to detect target by onlyreceiving the echo signal reflected by the target. Unlike the traditional radar, its receiveris absolute passive, making it far less vulnerable to electronic counter measures andanti-radiation guided missile, so this radar may have better survival in a hostileenvironment. In addition, PCL radar has better ability to detect low altitude target andstealth target by right of the signal characteristic of FM, television, etc. Therefore, PCLradar has become one of the most effective approaches to detect target undercomplicated electromagnetism environment, and it is valuable to study.
This dissertation addresses some aspects of PCL Radar for target localization andtracking. The research focus on the target localization precision, the influence of theconfiguration of PCL radar on target localization and tracking, the influence of glintnoise on target tracking, nonlinear and non-Gaussian filtering problem, andmaneuvering target tracking.
The main content of this dissertation is summarized as follows.
In the first part, the concept, the background, and the study value of PCL radar areintroduced, an overview of the development history and the available tracking methodsfor PCL radar is given. Then, we brief introduce the signal processing flow, the keytechnologies of the PCL radar, and the basic location methods. We also derive theKalman filter and the Extended Kalman filter based on the Bayesian theorem, and pointout the puzzle in PCL radar localization and tracking. At last, the particle filter-basedidea for target tracking of PCL radar is proposed, which form the basis of the followingstudy.
In the second part, the accuracy of target localization in PCL radar is studied. First,the precision of measurements parameter is analyzed, and the factors which affect theprecision of measurements parameter are pointed out. Then, geometrical dilution ofprecision (GDOP) of the target relative coordinate errors are formulated. The influenceof the target location method, the mode of location, and the configuration of PCL radaron the GDOP is analyzed. Finally, the location method and the mode of location whichsuit for PCL radar are found out.
In the third part, the tracking algorithm for PCL radar in the presence of non-Gaussian noise (glint noise) is studied. In PCL radar, the tracking performance ofEKF is affected seriously by the glint noise. To solve this problem, a new passive radartracking method is proposed based on particle filter (PF) and time of arrival (TOA)measurements. The method gains TOA measurements by multi-stations, utilizes aGaussian distribution and a student distribution to construct the glint distribution andthen uses nonlinear and non-Gaussian PF to track target, which avoids the error causedby EKF linearization. The simulations show that the new method overcomes the EKF,especially in glint noise environment. The real data farther demonstrates the validity ofthe proposed method.
In the fourth part, an improved particle filtering based on differential evolution(DE) algorithm is proposed. The traditional resampling scheme results in theimpoverishment phenomenon. To deal with this problem, the evolution idea isintroduced to the resampling scheme in particle filtering, and a new resampling schemebased on the DE algorithm is presented, in which the resampling process is regarded asa process for searching better particles. By using this scheme, an differential evolutionparticle filter is proposed, and then it is applied to the target tracking in PCL radar. Theperformance of the new PF and the standard PF is analyzed by the simulations, and thefactors affect the new PF are also analyzed. Simulation results demonstrate that theproposed PF outperforms the standard PF
In the fifth part, the differential evolution particle filter is further studied. Anunscented Kalman filter (UKF) is used to generate the importance proposal distribution(IPD) of particle filter, which matches the true posterior more closely and canincorporate the latest observations into a prior updating routine. In addition, fourdifferential aberrance operators are used in the DE resampling scheme, and four DEresampling schemes are presented. As a result, four types of differential evolutionparticle filters (DEPFs) are proposed, in which the UKF is utilised to generate the IPDand the DE resampling schemes are used as the resampling scheme. Simulation resultsdemonstrate that the proposed DEPFs outperform the basic differential evolutionparticle filter, and the unscented particle filter.
In the sixth part, the problem of manoeuvring target tracking in PCL radar withglint noise is studied. The dynamic state space (DSS) model of manoeuvring target isconstructed. To deal with the problem of manoeuvring target tracking in the presence ofglint noise, an interacting multiple model (IMM) particle filtering method usingmultiple TOA measurements from several transmitter-receiver pairs is proposed andevaluated. The influence of glint noise and configuration of transmitters and receiver stations on the tracking method are analyzed. Simulations illustrate that, compared tothe IMM and the standard PF, the proposed method obtains better estimates of position,velocity, and acceleration, and is smaller affected by the glint noise and configuration ofthe PCL radar.
引文
[1] Griffiths H. D., and Long N. R. W. Television-based bistatic radar[J]. IEEEProceeding Part F,1986,133(7):649–657.
[2] Howland P. E. Target tracking using television-based bistatic radar[J]. IEE Proc.Radar, Sonar and Navigation,1999,146(3):166–174.
[3]王俊,张守宏,保铮.基于外照射的无源相干雷达系统及其关键问题[J].电波科学学报.2005,20(3):381–385.
[4]陈伯孝,徐辉,张守宏.舰载无源综合脉冲孔径雷达及其关键问题[J].电子学报.2003,31(12):1776–1779.
[5]王小谟,吴曼青,王政.未来战争中的“沉默哨兵”—外辐射源目标探测与跟踪雷达[J].现代军事,2000年10月:10–12.
[6] Howland P. E., Maksimiuk D., and Reitsma G. FM radio based bistatic radar[J].IEE Proc. Radar, Sonar and Navigation,2005,152(3):107–115.
[7] Malanowski M.., Kulpa K., and Misiurewicz J.. PaRaDe-Passive radardemonstrator family development at warsaw university of technology[C]. MRRSSymposium Proceeding, Kiev, Ukraine,2008:75–78.
[8] Hongbo Sun, K. P. Danny, and Yilong Lu. Aircraft target measurments using aGSM-based passive radar[C]. IEEE Radar Conference, Rome, May2008:66–78.
[9] Conti M., Berizzi F., Petri D., et al. High range resolution DVB-T passiveradar[C]. Proceedings of the7thEuropean Radar Conference, Paris, France,2010:109–112.
[10]董智文,屈晓光.防空制导雷达反隐身性能分析[C].第十届全国雷达学术年会,2008:376-379.
[11]陈长兴,巩林玉,班斐等.米波谐振雷达反隐身技术研究[J].舰船电子对抗,2009,32(4):34–37.
[12] Baker C. J., Griffiths H. D., and Papoutsis I. Passive coherent location radarsystem. Part2: Waveform properties[J]. IEE Proc. Radar, Sonar and Navigation,2005,152(3):160–168.
[13] Colone F., O’Hagan D. W., Lombardo P., et al. A multistage processing algorithmfor disturbance removal and target detection in passive bistatic radar[J]. IEEETrans. on Aerospace and Electronic Systems,2009,45(2):698–722.
[14]朱家兵,洪一.基于复倒谱技术的无源雷达直达波提纯方法[J].现代雷达2007,29(8):75–78.
[15]王俊,赵洪立,张守宏,保铮.非合作连续波雷达中存在强直达波和多径杂波的运动目标检测方法[J].电子学报,2005,33(3):419–422.
[16] Willis N. J. Bistatic radar[M]. USA: Artech House,1991.
[17] Price A. Instruments of darkness-the struggle for radar supremacy[M]. WilliamKimber and Co.Ltd,1967:216–218.
[18]贾玉贵,现代对空情报雷达[M].北京：国防工业出版社,2004.
[19] Marko A., John M., et al. Bistatic passive radar[P]. United States Patent,2812493,May21,1974.
[20] Griffiths H. D., Garnett A. J., et al. Bistatic radar using satellite borneilluminators of opportunity[C]. IEE International Radar Conference,1992:276–279.
[21] Poullin D., and Lesturgie M. Radar multistatic emission non-cooperatives[C].International Conference on Radar, Paris,1994:370–375.
[22] Howland P. E. A passive metric radar using a transmitter of opportunity[C].International Conference on Radar, Paris,1994:251–256.
[23] Sahr J. D. The Manastash Ridge Radar: A passive bistatic radar for upperatmospheric radio science[J]. Radio Science,1997,32(6):2345–2358.
[24] Lind F. D., Sahr J. D., and Gidner D. M. First passive radar observation ofE-region irregularities[J]. Geophysical research letters,1999,26(4):2155–2158.
[25] Nordwall B. D. Silent Sentry-A new type of radar[J]. Aviation Week&SpaceTechnology, November30,1998.
[26] Gershanoff H. Transmitterless radar in testing[J]. Journal of Electronic Defense.November,1998.
[27] Bender B. Surveillance system uses broadcast signals[J]. Journal of ElectronicDefense. November,1998.
[28] Cutaia N. and O’Sullivan J. A. Automatic target recognition using kinematicpriors[C]. Proceedings of33rdConference on Decisioin and Control,1994:3303–3307.
[29] Yong Wu. Investigation of passive radar imaging using wigner-villedistribution[D]. Master dissertation, University of Illinois,2001.
[30] Poullin D. On the use of COFDM modulation (DAB, DVB) for passive radarapplication. Symp.Passive Radar LPI (Low Probability of Intercept) RadioFrequency Sensors, NATO RTO,Warsaw, Poland,2001.
[31] Poullin D. Passive detection using digital broadcasters (DAB, DVB) withCOFDM modulation[J]. IEE Proc. Radar, Sonar and Navigation,2005,152(3):143–152.
[32] Saini R., and Cherniakov M.. DTV signal ambiguity function analysia for radarapplication[J]. IEE Proc. Radar, Sonar and Navigation,2005,152(3):133–142.
[33] Capria A., Conti M., Petri D., et al. Costal ship detection by DVB-T softwaredefined passive radar-experimental results[C]. Annual GTTI Meeting, Brescia,21–23, June2010.
[34] Tan D. K. P., Sun H., Lu Y., Lesturgie M., et al. Passive radar using global systemfor mobile communication signal: theory, implementation and measurements[J].IEE Proc.Radar Sonar and Navigation,2005,152(3):116–123.
[35] Maio A. D., Foglia G., Pasquino N., et al. Measurement and analysis of cluttersignal from GSM/DCS-based passive radar[C]. Radar Conference-Surveillancefor a Safer World, Bordeaux, October2009
[36]何遵文,牛佳敏. CDMA移动通性信号的无源探测性能研究[J].兵工学报,2008,29(3):296–299.
[37]王蕾,王俊,李涛.基于CDMA通性信号的无源雷达定位系统[J].火控雷达技术,2009,38(2):4–9.
[38] Peiguo Liu, and Jibin Liu. Analysis of passive targets detection using CDMAsignal[C]. IEEE Int. Workshop VLSI Design&Video Tech, Suzhou, China, May,2005:408–410.
[39] Guo H., Woodbridge K., and Baker C. J. Evaluation of WiFi BeaconTransmissions for Wireless Based Passive Radar[C]. IEEE Radar Conference,Rome, Italy.2008.
[40] Chetty K., Smith G.., Guo Hui, et al. Target detection in high clutter using passivebistatic WiFi radar[C]. IEEE Radar Conference, Pasadena, May2009.
[41] Mojarrabi B., Homer J., and Kubik K. Power budget study for passive targetdetection and imaging using secondary applications of GPS signals in bistaticradar systems[C]. IEEE International Geoscience and Remote SensingSymposium, Vol.1,2002:449–451.
[42] Brown A., and Mathews B. Test results from a novel passive bistatic GPS radarusing a phased sensor array[C]. Proceeding of ION NTM, San Diego, January2007:1–6.
[43]杨进佩.基于GPS的无源雷达技术研究[D].南京：南京理工大学博士学位论文,2006.
[44] Inggs M. Passive coherent location as cognitive radar[C]. International WD&DCnoference,2009:229–233.
[45] Kulpa K., and Malanowski M. The cocept of simple MIMO PCL radar[C].Proceedings of the5thEuropean Radar Conference, Amsterdam, October2008:240–243.
[46]徐伟杰,王俊.基于TOA测量的Tn-R型无源雷达目标跟踪算法[J].系统工程与电子技术,2010,32(3):512–517.
[47] Herman S., Moulin P. A particle filtering approach to FM-based passive radartracking and automatic target recognition[C]. Proceedings of IEEE AerospaceConference,2002:1789–1808.
[48] Tobias M., and Lanterman A. D.. Multitarget tracking using multiple bistaticrange measurements with probability hypothesis densities[C]. Proc. SPIE-Int.Soc. Opt. Eng.,2004:296–305
[49] Tobias M., and Lanterman A. D. Probability hypothesis density-based multitargettracking with bistatic range and Doppler observations[J]. IEE Proc. Radar, Sonarand Navigation,2005,152(3):195–205.
[50] Tobias M., and Lanterman A. D.. Techniques for birth-particle placement in theprobability hypothesis density particle filter applied to passive radar[J]. IETRadar, Sonar and Navigation,2008,2(5):351–365.
[51] Yin Xuefeng, Pedersen T., Blattnig P., et al. A single-stage target trackingalgorithm for multistatic DVB-T passive radar system[C]. Digital SignalProcessing Workshop and5th IEEE Signal Processing Education Workshop,2009:518–523.
[52]唐续.外辐射源定位跟踪技术研究[D].成都：电子科技大学博士学位论文,2011.
[53]王俊,水鹏朗,保铮,张守宏.基于分数延迟估计的外辐射源雷达杂波相消算法[J].西安电子科技大学学报,2005,32(3):378–382.
[54] Poor H. V. An introduction to signal detection and estimation[M]. New York:Sping-Verlag,1998.
[55]刘金荣.基于外辐射源的反辐射导弹检测与识别技术[D].西安:西安电子科技大学硕士学位论文,2009.
[56]王俊,保铮,张守宏.无源探测与跟踪雷达系统技术及其发展[J].雷达科学与技术,2004,2(3):129–135.
[57]朱家兵,洪一,陶亮等.基于自适应分数延迟估计的FM广播辐射源雷达直达波对消[J].电子与信息学报,2007,29(7):1674–1677.
[58] Saini R., Cherniakov M., and Lenive V. Direct path interference suppression inbistatic system DTV based radar. Proceedings of the International RadarConference. Adelaide, Australia, Sept.2003:309–314.
[59]朱家兵,洪一.基于复倒谱技术的无源雷达直达波提纯方法[J].现代雷达,2007,29(8):75–78.
[60] Colone F., Cardinali R., Lombardo P., et al. Space-time constant modulusalgorithm for multipath removal on the reference signal exploited by passivebistatic radar[J]. IET Radar, Sonar and Navigation,2008,3(3):253–264.
[61]李辉,何友,唐小明,宋杰.非合作双基地雷达中直达波信号的重构[J].系统工程与电子技术,2010,32(10):2025–2030.
[62]尚海燕,水鹏朗,张守宏等.基于时频和形态学滤波的长时间能量积累检测[J].西安交通大学学报,2006,40(10):1094–1102.
[63]强勇,焦李成,保铮.一种有效的用于雷达弱目标检测的算法[J].电子学报,2003,31(3):1–4.
[64]宋慧波,高梅国,田黎育等.一种基于动态规划法的雷达微弱多目标检测方法[J].电子学报,2006,34(12):2142–2145.
[65] Davey S. J., Rutten M. G.., and Cheung B. A comparison of detectionperformance for several track-before-detect algorithms[J]. EURASIP Journal onAdvances in Signal Processing, Volum2008:1–10.
[66] Su H-T., Wu T-P., Liu H-W., Bao Z. Rao-Blackwellised particle filter basedtrack-before-detect algorithm[J]. IET Signal Processing,2008,2(2):169–176.
[67]丁鹭飞,耿富录.雷达原理[M].西安电子科技大学出版社,2002年第3版.
[68]胡来招.无源定位[M].国防工业出版社,2004年第1版.
[69] Foy W. H.. Position location solutions by Taylor series estimation[J]. IEEE Trans.on Aerospace and Electronic Systems,1976,12(2):187–194.
[70] Chan Y. T., Ho K. C. A simple and efficient estimator for hyperbolic location[J].IEEE Trans. on Signal Processing,1994,42(8):1905–1915.
[71] Huang Z, Lu J. Total least squares and equilibration algorithm for rangedifference location[J]. Electronics Letters,2004,40(5):121–122.
[72] Petre S., L. Jian. Source localization from range-difference measurments[J].IEEE Signal proc. Mag,2006,63(11):63–65.
[73] Kalman P. E.. A new approach to linear filtering and prediction problems[J].Transactions of the ASME, Journal of Basic Engineering,1960,82:35–45.
[74] Bar-shalom Y, Li X R, Kirubarajan T. Estimation with application to tracking andnavigation: theory, algorithm, and software[M]. New York: Wiley,2001.
[75]胡洪涛,敬忠良,李安平,胡士强.非高斯条件下基于粒子滤波的目标跟踪[J].上海交通大学学报,2004,38(12):1996–1999.
[76]宋小全,孙仲康.非高斯噪声下的机动目标跟踪[J].电子学报,1998,26(9):40–46.
[77]王军,林强,米慈中等译.雷达手册[M].电子工业出版社,2003年第1版.
[78]杨振起,张永顺,骆永军.双（多）基地雷达系统[M].国防工业出版社,1998年第1版.
[79]王德纯,丁家会,程望东等.精密跟踪测量雷达技术[M].电子工业出版社,2006年第1版.
[80]屈晓旭,罗勇等译.电子战目标定位方法[M].电子工业出版社,2008年第1版.
[81]孙仲康,周一宇,何黎星.单多基地有源无源定位技术[M].北京:国防工业出版社,1996.
[82] Griffiths, H. D., and Baker, C. J. Passive coherent location radar systems. Part1:Performance prediction[J]. IEE Proc., Radar Sonar Navig.,2005,152(3):153–159
[83] Saini, R., and Cherniakov, M. DTV signal ambiguity function analysis for radarapplication[J]. IEE Proc., Radar Sonar Navig.,2005,152,(3), pp.133–142
[84] Ulrich R. O. N. Extending Range Coverage with GSM Passive Lozalization bySensor Fusion[C].11th International Radar Symposium,16-18June2010,Vilnius, Lithuania.
[85]安玮,孙仲康.利用多普勒频率变化率的单站无源定位测距技术[C].雷达无源定位跟踪技术研讨会论文集,北京,2001:41–45.
[86] Olama M. M., Djouadi S. M., Papageorgiou I. G., et al. Positon and VelocityTracking in Mobile Networks Using Particle and Kalman Filtering WithComparison[J]. IEEE Trans.on Vehicular Technology,2008,57(2):1001–1009.
[87]占荣辉,辛勤,万建伟.基于最优采样函数的粒子滤波算法与贝叶斯估计[J].信号处理,2008,24(2):260–261.
[88] Sanjeev Arulampalam M., Maskell S., Gordon N., et al. A Tutorial on ParticleFilters for Online Nonlinear/Non-Gaussian Bayesian Tracking[J]. IEEE Trans.onSignal Processing,2002,50(2):175–181.
[89] Merwe R., Doucet A., Freitas N., and Wan, E. The unscented particle filter[R].Technical report CUED/F-INFENG/TR380, Cambridge University EngineeringDepartment,2000:1–40.
[90] Hammersley J. M., Morton K. W. Poor man’s Monte Carlo[J]. Journal of theRoyal Statistical Society B,1954,16(1):23–38.
[91] Rosenbluth M. N., Rosenbluth A. W. Monte Carlo calculation of the averageextension of molecular chains[J]. J. Chemical Physics.1955,23:356–359.
[92] Handschin J. E., Mayne D. Q. Monte Carlo techniques to estimate the conditionalexpectation in multi-stage nonlinear filtering[J]. Int. J.Control.1969,9(5):547–559.
[93] Gordon N., Salmond D., Smith A. F. M. Novel approach to non-linear andnon-Gaussian Bayesian state estimation. Proc. Inst. Elect. Eng. F.1993,140:107–113
[94] Chen. Z. Bayesian Filtering: From Kalman filters to particle filters, andbeyond[M]. Hamilton: Mcmaser University,2003.
[95] Pitt M., Shephard N. Filtering via Simulation: Auxiliary Particle Filter[J]. Journalof the American Statistical Association.1999,94(446):590–599
[96] Carpenter J., Clifford P., Fearnhead P. Improved Particle Filter for Nonlinearproblems[J]. IEEE Proc.-Radar, Sonar Navigation.1999,146(1):2–7.
[97] Doucet A., N. de Freitas, et al. Rao-Blackwellised Particle Filtering for DynamicBayesian Networks[C]. In proceeding UAI, San Francisco.2000:176–783.
[98] Carlin B. P., Polson N. G., Stoffer D. S. A Monte Carlo Approach to Non-normaland Non-linear State-space Modeling[J]. Journal of the American StatisticalAssociation.1992,87(418):493–500.
[99] Avitzour D. A stochastic Simulation Bayesian Approach to Multi-targetTracking[J]. IEEE Proceeding-F.1995,142(2):41–44
[100] Doucet A., N. de Freitas, Gordon N., et al. Sequential Monte Carlo Methods inPractice[M]. Springer.2001,6.
[101] Ristic B., Arulampalam S., Gordon N. Beyond the Kalman Filter: Particles forTracking Application[M]. Norwood:Artech House,2004.
[102] Djuric P. M. Guest Editorial–Special Issue on Monte Carlo Methods forStatistical Signal Processing[J]. IEEE Transactions on Signal Processing.2002,50(2):173
[103] Haykin S., N. de Freitas. Special Issue on Sequential State Estimation.Proceedings of the IEEE[J].2004,92(3):399–400
[104] Djuric P. M., Kotecha J. H., Zhang Jianqui, et al. Particle filtering: a review of thetheory and how it can be used for solving problems in wireless communication[J]. IEEE Signal Processing Magazine,2003,20(5):19–38.
[105]刘喜梅,魏婉韵,于飞.基于粒子滤波的分布式故障诊断[J].传感器与微系统.2008,27(3):30–33.
[106]朱林富，张三同.基于改进粒子滤波和平均代价的故障诊断方法研究[J].电子测量与仪器学报.2010,24(1):66–71.
[107] Sheu H. J., Liu C. L. The credit risk pricing with particle filter approach[C].Proceedings of the9th Joint Conference on Information Systems. Kaohsiung:Atlantis Press,2006.
[108]王法胜,张应博,董宗然.基于混合卡尔曼粒子滤波算法的期权定价方法[J].计算机应用,2009,29(12):3406–3408.
[109] Chang Wenyan, Chen Chusong, and Jian Yongdian. Visual tracking inHigh-Dimensional state space by Appearance-Guided particle filtering[J]. IEEEtransaction on image processing,2008,17(7):1154–1167.
[110]李雁斌,曹作良,刘常杰,叶声华.基于粒子滤波的全方位视觉传感器实现移动机器人导航[J].传感技术学报,2009,22(5):745–750.
[111] Mei Zhao, Santong Zhang, Gang Zhu. The application reserch of unscentedparticle filter algorithm to GPS/DR[C]. Proceedings of the6th World Congresson Intelligent Control and Automation, June,2006, Dalian, China:8717–8721.
[112]侯代文,殷福亮.基于粒子滤波的交互式多模型说话人跟踪方法[J].电子学报,2010,38(4):835–841.
[113]李翠芸,姬红兵. Rao-Blackwellized粒子滤波的红外多个弱目标检测前跟踪[J].光学精密工程,2009,17(9):2342–2349.
[114]徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社.第一版.1985.
[115] Yardim C, Gerstoft P, Hodgkiss W S. Tracking Refractivity from Clutter UsingKalman and Particle Filters[J]. IEEE Trans.on Antennas and Propagation,2008,56(4):1060-1069.
[116] Kong A., Liu J. S., Wong W. H. Sequential imputations and Bayesian missingdataproblems[J]. J. American Statistical Association.1994:278–288.
[117] Kitagawa G. Monte Carlo filter and smoother for non-Gaussian nonlinear statespace models[J]. J of Computational and Graphical Statistics,1996,5(1):1–25.
[118]李红伟,王俊,刘玉春.粒子滤波和多站TOA的外辐射源雷达跟踪算法.系统工程与电子技术.2010,32(11):2263–2267.
[119]李红伟,王俊,刘玉春.两种外辐射源雷达跟踪算法性能分析.西安电子科技大学学报.2010,37(6):1048–1052.
[120] Skolnik M. I. Radar Handbook[M]. McGraw-Hill Companies, Second Edition,2003.
[121] Kostantinos N. P., Dimitris H. Advanced signal processing handbook[M]. BocaRaton: CRC Press LLC,2001.
[122] Gustafsson F., Gunnarsson F., Bergman N., et al. Particle Filters forPositioningNavigation and Tracking [J]. IEEE Transactions on Signal Processing,2002,50(2):425–437.
[123] Aswin C. S., Ankur S, Rama C. Alogrithmic and Architectural Optimizations forComputationally Efficient Particle Filtering [J]. IEEE Transactions on ImageProcessing,2008,17(5):737–739.
[124] Higuchi T. Monte Carlo filtering using genetics algorithm operators[J]. Journal ofStatistical Computation and Simulation,1997,59(l):1–23.
[125]杨璐,李明,张鹏.一种新的改进粒子滤波算法[J].西安电子科技大学学报,2010,37(5):862–865.
[126]程水英,张剑云.裂变自举粒子滤波[J].电子学报,2008,36(3):500–504.
[127]莫以为,萧德云.进化粒子滤波算法及其应用[J].控制理论与应用,2005,22(2):269–272.
[128] Chakraborty U. K. Advances in differential evolution[M]. Berlin. Heidelberg,Springer-Verlag,2008:1–27.
[129]武志峰,黄厚宽,张莹等.基于自适应差异演化的模糊聚类算法[J].北京交通大学学报,2009,33(2):17–21.
[130]邓长寿,赵秉岩,梁昌勇.改进的差异演化算法[J].计算机工程,2009,35(24):194–196.
[131] Price K. V., Storn R. M., Lampinen J.A. Differential evolution: A practicalapproach to global optimization[M]. Berlin. Heidelberg, Springer-Verlag,2005.
[132]谢晓锋,张文俊,张国瑞等.差分演化的实验研究[J].控制与决策,2004,19(1):49–56.
[133] Pardalos P. M. Differential evolution: In search of solutions[M]. SpringerScience+Business Media, LLC,2006.
[134] Price K., and Storn R. Differential evolution: A simple evolution strategy for fastoptimization[J]. Dr. Dobb’s Journal of Software Tools,1997,22(4):18–24.
[135] Price K., and Storn R. Differential evolution: A simple and efficient heuristic forglobal optimization over continuous spaces[J]. Journal of Global Optimization,December,1997(11):341–359.
[136] Price K.V. chapter Differential Evolution, New Ideas in Optimization[M].London, McGraw-Hill,1999.
[137] Musso, C., Oudjane, N., and Legland, F.:‘Improving regularised particle filter’,in Doucet, A., de Freitas, J.F.G., Gordon, N.J.(Eds):‘Sequential Monte Carlomethods’(Springer-Verlag,2001), pp.247–272
[138] Casella G., Robert C. P. Rao-Blackwellisation of samplings chemes[J].Biometrika,1996,83(1):81–94
[139] De Freitas, J. F. G., Niranjan, M., Gee, A. H., and Doucet, A. Sequential MonteCarlo methods to train neural network models[J], Neural Comput.,2000,12(4):955–993.
[140] Merwe, R., Doucet, A., Freitas, N., and Wan, E.:‘The unscented particle filter’,Technical report CUED/F-INFENG/TR380, Cambridge University,2000:1–40.
[141]袁泽剑,郑南宁,贾新春.高斯–厄米特粒子滤波器[J].电子学报,2003,31(7):970–973.
[142]梁军利,杨树元,曲超,高丽.一种新的基于数值积分的粒子滤波算法[J].电子与信息学报,2007,29(6):1369–1372.
[143] Julier S. J., Uhlmann J. K. A general method for approximating nonlineartransformations of probability distributions[R]. Dept. of Engineering Science,University of Oxford,1996.
[144] Julier S. J. The scaled unscented transformation[C]. Proceedings of the AmericanControl Conference,2002:4555–4559.
[145] Jazwinski, A.:‘Stochastic processes and filtering theory’(New York: AcademicPress,1970)
[146] Wan, E., and Van der Merwe, R. The unscented Kalman filter for nonlinearestimation[J]. In Proceedings of Adaptive Systems for Signal Processing,Communications, and Control Symposium,2000:53–158.
[147] Skolnik, M.I. Radar Handbook[M]. The McGraw-Hill Co. and Publishing Houseof Electronics Industry Press,2nd edition,2003:726–734.
[148] Hewer, G., Martin, R., and Zeh, J. Robust preprocessing for Kalman filtering ofglint noise[J]. IEEE Trans. Aerosp. Electron. Syst.,1987,23:120–128.
[149] Borden, B., Mumford, M.A. Statistical glint/radar cross section target model[J].IEEE Trans. Aerosp. Electron. Syst.,1983, AES-19,(1):781–785.
[150] Wu, W., and Cheng, P. A nonlinear IMM algorithm for maneuvering targettracking[J]. IEEE Trans. Aerosp. Electron. Syst.,1994,30,(3):875–884.
[151] Blom, H., and Bar-Shalom, Y. The interacting multiple model algorithm withMarkovian switching coefficients[J]. IEEE Trans. Automatic Control.1988,33,(8):780–783.
[152] Mazor, E., Averbuch, A., Bar-Shalom, Y., and Dayan, J. Interacting multiplemodel methods in target tracking: A survey[J]. IEEE Trans. Aerosp. Electron.Syst.,1998,34,(1):103–123
[153] Boers, Y., and Driessen, J.N. Interacting multiple model particle filter[J]. IEEProc., Radar Sonar Navig.,2003,150,(5):344–349.
[154] Guo Ronghua, Qin Zheng, and Li Xiangnan, et al. Interacting multiple modelparticle-type filtering approaches to ground target tracking[J]. Journal ofComputers,2008,3,(7):23–30.
[155] Wang Da-bao, Liu Shang-qian, Wang Hui-feng. Mono-station passive locationalgorithm based on particle filter and interacting multiple model[J]. SignalProcessing (Chinese),2008,25,(10):1566–1568.
[156] Petsios, M.N., Alivizatos, E.G., and Uzunoglu, N.K. Manoeuvring target trackingusing multiple bistatic range and range-rate measurements[J], Signal Processing,2007,87:665–686.
[157] Cardinali, R., Colone, F., Lombardo, P., Crognale, O., and Cosmi, A. Mutipathcancellation on reference antenna for passive radar which exploits FMtransmission[J]. IET International Radar Conference, Edinburgh, UK, October2007:15–18.
[158] Zhao Hong-li, Wu Tie-ping, Bao Zheng. The method for improving the rangeresolution in a passive location system[J]. Journal of Xidian University,2006,33,(2):165–168.
[159] Bongioanni, C., Colone, F., Lombardo, P. Performance Analysis of aMulti-Frequency FM Based Passive Bistatic Radar[J]. IEEE Radar Conference,May2008:1–6.
[160] Christian R. B., Shengli Zhou and Peter W. Signal extraction using compressedsensing for passive radar with OFDM signals[C].11thInternational Conferenceon Information Fusion2008:1–6
[161] Blom, H. A., Bloem, E. A. Exact Bayesian and particle filtering of stochastichybrid systems[J]. IEEE Trans. Aerosp. Electron. Syst.,2007,43,(1):55–70.
[162] Blom, H. A. P. An efficient filter for abruptly changing systems[J]. InProceedings of the23rd IEEE Conference on Decision and Control, Las Vegas,NV, Dec.1984.
[163] Foo, P. H., and Ng, G. W. Combining the interacting multiple model method withparticle filters for manoeuvring target tracking[J]. IET Radar Sonar Navig.,2011,5,(3):234–255.
[164] Doucet, A. and Ristic, B.:’Recursive state estimation for multiple switchingmodels with unknown tansition probabilities.’ IEEE Trans. Aerosp. Electron.Syst.,2002,38,(3), pp.1098–1104
[2] Howland P. E. Target tracking using television-based bistatic radar[J]. IEE Proc.Radar, Sonar and Navigation,1999,146(3):166–174.
[3]王俊,张守宏,保铮.基于外照射的无源相干雷达系统及其关键问题[J].电波科学学报.2005,20(3):381–385.
[4]陈伯孝,徐辉,张守宏.舰载无源综合脉冲孔径雷达及其关键问题[J].电子学报.2003,31(12):1776–1779.
[5]王小谟,吴曼青,王政.未来战争中的“沉默哨兵”—外辐射源目标探测与跟踪雷达[J].现代军事,2000年10月:10–12.
[6] Howland P. E., Maksimiuk D., and Reitsma G. FM radio based bistatic radar[J].IEE Proc. Radar, Sonar and Navigation,2005,152(3):107–115.
[7] Malanowski M.., Kulpa K., and Misiurewicz J.. PaRaDe-Passive radardemonstrator family development at warsaw university of technology[C]. MRRSSymposium Proceeding, Kiev, Ukraine,2008:75–78.
[8] Hongbo Sun, K. P. Danny, and Yilong Lu. Aircraft target measurments using aGSM-based passive radar[C]. IEEE Radar Conference, Rome, May2008:66–78.
[9] Conti M., Berizzi F., Petri D., et al. High range resolution DVB-T passiveradar[C]. Proceedings of the7thEuropean Radar Conference, Paris, France,2010:109–112.
[10]董智文,屈晓光.防空制导雷达反隐身性能分析[C].第十届全国雷达学术年会,2008:376-379.
[11]陈长兴,巩林玉,班斐等.米波谐振雷达反隐身技术研究[J].舰船电子对抗,2009,32(4):34–37.
[12] Baker C. J., Griffiths H. D., and Papoutsis I. Passive coherent location radarsystem. Part2: Waveform properties[J]. IEE Proc. Radar, Sonar and Navigation,2005,152(3):160–168.
[13] Colone F., O’Hagan D. W., Lombardo P., et al. A multistage processing algorithmfor disturbance removal and target detection in passive bistatic radar[J]. IEEETrans. on Aerospace and Electronic Systems,2009,45(2):698–722.
[14]朱家兵,洪一.基于复倒谱技术的无源雷达直达波提纯方法[J].现代雷达2007,29(8):75–78.
[15]王俊,赵洪立,张守宏,保铮.非合作连续波雷达中存在强直达波和多径杂波的运动目标检测方法[J].电子学报,2005,33(3):419–422.
[16] Willis N. J. Bistatic radar[M]. USA: Artech House,1991.
[17] Price A. Instruments of darkness-the struggle for radar supremacy[M]. WilliamKimber and Co.Ltd,1967:216–218.
[18]贾玉贵,现代对空情报雷达[M].北京：国防工业出版社,2004.
[19] Marko A., John M., et al. Bistatic passive radar[P]. United States Patent,2812493,May21,1974.
[20] Griffiths H. D., Garnett A. J., et al. Bistatic radar using satellite borneilluminators of opportunity[C]. IEE International Radar Conference,1992:276–279.
[21] Poullin D., and Lesturgie M. Radar multistatic emission non-cooperatives[C].International Conference on Radar, Paris,1994:370–375.
[22] Howland P. E. A passive metric radar using a transmitter of opportunity[C].International Conference on Radar, Paris,1994:251–256.
[23] Sahr J. D. The Manastash Ridge Radar: A passive bistatic radar for upperatmospheric radio science[J]. Radio Science,1997,32(6):2345–2358.
[24] Lind F. D., Sahr J. D., and Gidner D. M. First passive radar observation ofE-region irregularities[J]. Geophysical research letters,1999,26(4):2155–2158.
[25] Nordwall B. D. Silent Sentry-A new type of radar[J]. Aviation Week&SpaceTechnology, November30,1998.
[26] Gershanoff H. Transmitterless radar in testing[J]. Journal of Electronic Defense.November,1998.
[27] Bender B. Surveillance system uses broadcast signals[J]. Journal of ElectronicDefense. November,1998.
[28] Cutaia N. and O’Sullivan J. A. Automatic target recognition using kinematicpriors[C]. Proceedings of33rdConference on Decisioin and Control,1994:3303–3307.
[29] Yong Wu. Investigation of passive radar imaging using wigner-villedistribution[D]. Master dissertation, University of Illinois,2001.
[30] Poullin D. On the use of COFDM modulation (DAB, DVB) for passive radarapplication. Symp.Passive Radar LPI (Low Probability of Intercept) RadioFrequency Sensors, NATO RTO,Warsaw, Poland,2001.
[31] Poullin D. Passive detection using digital broadcasters (DAB, DVB) withCOFDM modulation[J]. IEE Proc. Radar, Sonar and Navigation,2005,152(3):143–152.
[32] Saini R., and Cherniakov M.. DTV signal ambiguity function analysia for radarapplication[J]. IEE Proc. Radar, Sonar and Navigation,2005,152(3):133–142.
[33] Capria A., Conti M., Petri D., et al. Costal ship detection by DVB-T softwaredefined passive radar-experimental results[C]. Annual GTTI Meeting, Brescia,21–23, June2010.
[34] Tan D. K. P., Sun H., Lu Y., Lesturgie M., et al. Passive radar using global systemfor mobile communication signal: theory, implementation and measurements[J].IEE Proc.Radar Sonar and Navigation,2005,152(3):116–123.
[35] Maio A. D., Foglia G., Pasquino N., et al. Measurement and analysis of cluttersignal from GSM/DCS-based passive radar[C]. Radar Conference-Surveillancefor a Safer World, Bordeaux, October2009
[36]何遵文,牛佳敏. CDMA移动通性信号的无源探测性能研究[J].兵工学报,2008,29(3):296–299.
[37]王蕾,王俊,李涛.基于CDMA通性信号的无源雷达定位系统[J].火控雷达技术,2009,38(2):4–9.
[38] Peiguo Liu, and Jibin Liu. Analysis of passive targets detection using CDMAsignal[C]. IEEE Int. Workshop VLSI Design&Video Tech, Suzhou, China, May,2005:408–410.
[39] Guo H., Woodbridge K., and Baker C. J. Evaluation of WiFi BeaconTransmissions for Wireless Based Passive Radar[C]. IEEE Radar Conference,Rome, Italy.2008.
[40] Chetty K., Smith G.., Guo Hui, et al. Target detection in high clutter using passivebistatic WiFi radar[C]. IEEE Radar Conference, Pasadena, May2009.
[41] Mojarrabi B., Homer J., and Kubik K. Power budget study for passive targetdetection and imaging using secondary applications of GPS signals in bistaticradar systems[C]. IEEE International Geoscience and Remote SensingSymposium, Vol.1,2002:449–451.
[42] Brown A., and Mathews B. Test results from a novel passive bistatic GPS radarusing a phased sensor array[C]. Proceeding of ION NTM, San Diego, January2007:1–6.
[43]杨进佩.基于GPS的无源雷达技术研究[D].南京：南京理工大学博士学位论文,2006.
[44] Inggs M. Passive coherent location as cognitive radar[C]. International WD&DCnoference,2009:229–233.
[45] Kulpa K., and Malanowski M. The cocept of simple MIMO PCL radar[C].Proceedings of the5thEuropean Radar Conference, Amsterdam, October2008:240–243.
[46]徐伟杰,王俊.基于TOA测量的Tn-R型无源雷达目标跟踪算法[J].系统工程与电子技术,2010,32(3):512–517.
[47] Herman S., Moulin P. A particle filtering approach to FM-based passive radartracking and automatic target recognition[C]. Proceedings of IEEE AerospaceConference,2002:1789–1808.
[48] Tobias M., and Lanterman A. D.. Multitarget tracking using multiple bistaticrange measurements with probability hypothesis densities[C]. Proc. SPIE-Int.Soc. Opt. Eng.,2004:296–305
[49] Tobias M., and Lanterman A. D. Probability hypothesis density-based multitargettracking with bistatic range and Doppler observations[J]. IEE Proc. Radar, Sonarand Navigation,2005,152(3):195–205.
[50] Tobias M., and Lanterman A. D.. Techniques for birth-particle placement in theprobability hypothesis density particle filter applied to passive radar[J]. IETRadar, Sonar and Navigation,2008,2(5):351–365.
[51] Yin Xuefeng, Pedersen T., Blattnig P., et al. A single-stage target trackingalgorithm for multistatic DVB-T passive radar system[C]. Digital SignalProcessing Workshop and5th IEEE Signal Processing Education Workshop,2009:518–523.
[52]唐续.外辐射源定位跟踪技术研究[D].成都：电子科技大学博士学位论文,2011.
[53]王俊,水鹏朗,保铮,张守宏.基于分数延迟估计的外辐射源雷达杂波相消算法[J].西安电子科技大学学报,2005,32(3):378–382.
[54] Poor H. V. An introduction to signal detection and estimation[M]. New York:Sping-Verlag,1998.
[55]刘金荣.基于外辐射源的反辐射导弹检测与识别技术[D].西安:西安电子科技大学硕士学位论文,2009.
[56]王俊,保铮,张守宏.无源探测与跟踪雷达系统技术及其发展[J].雷达科学与技术,2004,2(3):129–135.
[57]朱家兵,洪一,陶亮等.基于自适应分数延迟估计的FM广播辐射源雷达直达波对消[J].电子与信息学报,2007,29(7):1674–1677.
[58] Saini R., Cherniakov M., and Lenive V. Direct path interference suppression inbistatic system DTV based radar. Proceedings of the International RadarConference. Adelaide, Australia, Sept.2003:309–314.
[59]朱家兵,洪一.基于复倒谱技术的无源雷达直达波提纯方法[J].现代雷达,2007,29(8):75–78.
[60] Colone F., Cardinali R., Lombardo P., et al. Space-time constant modulusalgorithm for multipath removal on the reference signal exploited by passivebistatic radar[J]. IET Radar, Sonar and Navigation,2008,3(3):253–264.
[61]李辉,何友,唐小明,宋杰.非合作双基地雷达中直达波信号的重构[J].系统工程与电子技术,2010,32(10):2025–2030.
[62]尚海燕,水鹏朗,张守宏等.基于时频和形态学滤波的长时间能量积累检测[J].西安交通大学学报,2006,40(10):1094–1102.
[63]强勇,焦李成,保铮.一种有效的用于雷达弱目标检测的算法[J].电子学报,2003,31(3):1–4.
[64]宋慧波,高梅国,田黎育等.一种基于动态规划法的雷达微弱多目标检测方法[J].电子学报,2006,34(12):2142–2145.
[65] Davey S. J., Rutten M. G.., and Cheung B. A comparison of detectionperformance for several track-before-detect algorithms[J]. EURASIP Journal onAdvances in Signal Processing, Volum2008:1–10.
[66] Su H-T., Wu T-P., Liu H-W., Bao Z. Rao-Blackwellised particle filter basedtrack-before-detect algorithm[J]. IET Signal Processing,2008,2(2):169–176.
[67]丁鹭飞,耿富录.雷达原理[M].西安电子科技大学出版社,2002年第3版.
[68]胡来招.无源定位[M].国防工业出版社,2004年第1版.
[69] Foy W. H.. Position location solutions by Taylor series estimation[J]. IEEE Trans.on Aerospace and Electronic Systems,1976,12(2):187–194.
[70] Chan Y. T., Ho K. C. A simple and efficient estimator for hyperbolic location[J].IEEE Trans. on Signal Processing,1994,42(8):1905–1915.
[71] Huang Z, Lu J. Total least squares and equilibration algorithm for rangedifference location[J]. Electronics Letters,2004,40(5):121–122.
[72] Petre S., L. Jian. Source localization from range-difference measurments[J].IEEE Signal proc. Mag,2006,63(11):63–65.
[73] Kalman P. E.. A new approach to linear filtering and prediction problems[J].Transactions of the ASME, Journal of Basic Engineering,1960,82:35–45.
[74] Bar-shalom Y, Li X R, Kirubarajan T. Estimation with application to tracking andnavigation: theory, algorithm, and software[M]. New York: Wiley,2001.
[75]胡洪涛,敬忠良,李安平,胡士强.非高斯条件下基于粒子滤波的目标跟踪[J].上海交通大学学报,2004,38(12):1996–1999.
[76]宋小全,孙仲康.非高斯噪声下的机动目标跟踪[J].电子学报,1998,26(9):40–46.
[77]王军,林强,米慈中等译.雷达手册[M].电子工业出版社,2003年第1版.
[78]杨振起,张永顺,骆永军.双（多）基地雷达系统[M].国防工业出版社,1998年第1版.
[79]王德纯,丁家会,程望东等.精密跟踪测量雷达技术[M].电子工业出版社,2006年第1版.
[80]屈晓旭,罗勇等译.电子战目标定位方法[M].电子工业出版社,2008年第1版.
[81]孙仲康,周一宇,何黎星.单多基地有源无源定位技术[M].北京:国防工业出版社,1996.
[82] Griffiths, H. D., and Baker, C. J. Passive coherent location radar systems. Part1:Performance prediction[J]. IEE Proc., Radar Sonar Navig.,2005,152(3):153–159
[83] Saini, R., and Cherniakov, M. DTV signal ambiguity function analysis for radarapplication[J]. IEE Proc., Radar Sonar Navig.,2005,152,(3), pp.133–142
[84] Ulrich R. O. N. Extending Range Coverage with GSM Passive Lozalization bySensor Fusion[C].11th International Radar Symposium,16-18June2010,Vilnius, Lithuania.
[85]安玮,孙仲康.利用多普勒频率变化率的单站无源定位测距技术[C].雷达无源定位跟踪技术研讨会论文集,北京,2001:41–45.
[86] Olama M. M., Djouadi S. M., Papageorgiou I. G., et al. Positon and VelocityTracking in Mobile Networks Using Particle and Kalman Filtering WithComparison[J]. IEEE Trans.on Vehicular Technology,2008,57(2):1001–1009.
[87]占荣辉,辛勤,万建伟.基于最优采样函数的粒子滤波算法与贝叶斯估计[J].信号处理,2008,24(2):260–261.
[88] Sanjeev Arulampalam M., Maskell S., Gordon N., et al. A Tutorial on ParticleFilters for Online Nonlinear/Non-Gaussian Bayesian Tracking[J]. IEEE Trans.onSignal Processing,2002,50(2):175–181.
[89] Merwe R., Doucet A., Freitas N., and Wan, E. The unscented particle filter[R].Technical report CUED/F-INFENG/TR380, Cambridge University EngineeringDepartment,2000:1–40.
[90] Hammersley J. M., Morton K. W. Poor man’s Monte Carlo[J]. Journal of theRoyal Statistical Society B,1954,16(1):23–38.
[91] Rosenbluth M. N., Rosenbluth A. W. Monte Carlo calculation of the averageextension of molecular chains[J]. J. Chemical Physics.1955,23:356–359.
[92] Handschin J. E., Mayne D. Q. Monte Carlo techniques to estimate the conditionalexpectation in multi-stage nonlinear filtering[J]. Int. J.Control.1969,9(5):547–559.
[93] Gordon N., Salmond D., Smith A. F. M. Novel approach to non-linear andnon-Gaussian Bayesian state estimation. Proc. Inst. Elect. Eng. F.1993,140:107–113
[94] Chen. Z. Bayesian Filtering: From Kalman filters to particle filters, andbeyond[M]. Hamilton: Mcmaser University,2003.
[95] Pitt M., Shephard N. Filtering via Simulation: Auxiliary Particle Filter[J]. Journalof the American Statistical Association.1999,94(446):590–599
[96] Carpenter J., Clifford P., Fearnhead P. Improved Particle Filter for Nonlinearproblems[J]. IEEE Proc.-Radar, Sonar Navigation.1999,146(1):2–7.
[97] Doucet A., N. de Freitas, et al. Rao-Blackwellised Particle Filtering for DynamicBayesian Networks[C]. In proceeding UAI, San Francisco.2000:176–783.
[98] Carlin B. P., Polson N. G., Stoffer D. S. A Monte Carlo Approach to Non-normaland Non-linear State-space Modeling[J]. Journal of the American StatisticalAssociation.1992,87(418):493–500.
[99] Avitzour D. A stochastic Simulation Bayesian Approach to Multi-targetTracking[J]. IEEE Proceeding-F.1995,142(2):41–44
[100] Doucet A., N. de Freitas, Gordon N., et al. Sequential Monte Carlo Methods inPractice[M]. Springer.2001,6.
[101] Ristic B., Arulampalam S., Gordon N. Beyond the Kalman Filter: Particles forTracking Application[M]. Norwood:Artech House,2004.
[102] Djuric P. M. Guest Editorial–Special Issue on Monte Carlo Methods forStatistical Signal Processing[J]. IEEE Transactions on Signal Processing.2002,50(2):173
[103] Haykin S., N. de Freitas. Special Issue on Sequential State Estimation.Proceedings of the IEEE[J].2004,92(3):399–400
[104] Djuric P. M., Kotecha J. H., Zhang Jianqui, et al. Particle filtering: a review of thetheory and how it can be used for solving problems in wireless communication[J]. IEEE Signal Processing Magazine,2003,20(5):19–38.
[105]刘喜梅,魏婉韵,于飞.基于粒子滤波的分布式故障诊断[J].传感器与微系统.2008,27(3):30–33.
[106]朱林富，张三同.基于改进粒子滤波和平均代价的故障诊断方法研究[J].电子测量与仪器学报.2010,24(1):66–71.
[107] Sheu H. J., Liu C. L. The credit risk pricing with particle filter approach[C].Proceedings of the9th Joint Conference on Information Systems. Kaohsiung:Atlantis Press,2006.
[108]王法胜,张应博,董宗然.基于混合卡尔曼粒子滤波算法的期权定价方法[J].计算机应用,2009,29(12):3406–3408.
[109] Chang Wenyan, Chen Chusong, and Jian Yongdian. Visual tracking inHigh-Dimensional state space by Appearance-Guided particle filtering[J]. IEEEtransaction on image processing,2008,17(7):1154–1167.
[110]李雁斌,曹作良,刘常杰,叶声华.基于粒子滤波的全方位视觉传感器实现移动机器人导航[J].传感技术学报,2009,22(5):745–750.
[111] Mei Zhao, Santong Zhang, Gang Zhu. The application reserch of unscentedparticle filter algorithm to GPS/DR[C]. Proceedings of the6th World Congresson Intelligent Control and Automation, June,2006, Dalian, China:8717–8721.
[112]侯代文,殷福亮.基于粒子滤波的交互式多模型说话人跟踪方法[J].电子学报,2010,38(4):835–841.
[113]李翠芸,姬红兵. Rao-Blackwellized粒子滤波的红外多个弱目标检测前跟踪[J].光学精密工程,2009,17(9):2342–2349.
[114]徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社.第一版.1985.
[115] Yardim C, Gerstoft P, Hodgkiss W S. Tracking Refractivity from Clutter UsingKalman and Particle Filters[J]. IEEE Trans.on Antennas and Propagation,2008,56(4):1060-1069.
[116] Kong A., Liu J. S., Wong W. H. Sequential imputations and Bayesian missingdataproblems[J]. J. American Statistical Association.1994:278–288.
[117] Kitagawa G. Monte Carlo filter and smoother for non-Gaussian nonlinear statespace models[J]. J of Computational and Graphical Statistics,1996,5(1):1–25.
[118]李红伟,王俊,刘玉春.粒子滤波和多站TOA的外辐射源雷达跟踪算法.系统工程与电子技术.2010,32(11):2263–2267.
[119]李红伟,王俊,刘玉春.两种外辐射源雷达跟踪算法性能分析.西安电子科技大学学报.2010,37(6):1048–1052.
[120] Skolnik M. I. Radar Handbook[M]. McGraw-Hill Companies, Second Edition,2003.
[121] Kostantinos N. P., Dimitris H. Advanced signal processing handbook[M]. BocaRaton: CRC Press LLC,2001.
[122] Gustafsson F., Gunnarsson F., Bergman N., et al. Particle Filters forPositioningNavigation and Tracking [J]. IEEE Transactions on Signal Processing,2002,50(2):425–437.
[123] Aswin C. S., Ankur S, Rama C. Alogrithmic and Architectural Optimizations forComputationally Efficient Particle Filtering [J]. IEEE Transactions on ImageProcessing,2008,17(5):737–739.
[124] Higuchi T. Monte Carlo filtering using genetics algorithm operators[J]. Journal ofStatistical Computation and Simulation,1997,59(l):1–23.
[125]杨璐,李明,张鹏.一种新的改进粒子滤波算法[J].西安电子科技大学学报,2010,37(5):862–865.
[126]程水英,张剑云.裂变自举粒子滤波[J].电子学报,2008,36(3):500–504.
[127]莫以为,萧德云.进化粒子滤波算法及其应用[J].控制理论与应用,2005,22(2):269–272.
[128] Chakraborty U. K. Advances in differential evolution[M]. Berlin. Heidelberg,Springer-Verlag,2008:1–27.
[129]武志峰,黄厚宽,张莹等.基于自适应差异演化的模糊聚类算法[J].北京交通大学学报,2009,33(2):17–21.
[130]邓长寿,赵秉岩,梁昌勇.改进的差异演化算法[J].计算机工程,2009,35(24):194–196.
[131] Price K. V., Storn R. M., Lampinen J.A. Differential evolution: A practicalapproach to global optimization[M]. Berlin. Heidelberg, Springer-Verlag,2005.
[132]谢晓锋,张文俊,张国瑞等.差分演化的实验研究[J].控制与决策,2004,19(1):49–56.
[133] Pardalos P. M. Differential evolution: In search of solutions[M]. SpringerScience+Business Media, LLC,2006.
[134] Price K., and Storn R. Differential evolution: A simple evolution strategy for fastoptimization[J]. Dr. Dobb’s Journal of Software Tools,1997,22(4):18–24.
[135] Price K., and Storn R. Differential evolution: A simple and efficient heuristic forglobal optimization over continuous spaces[J]. Journal of Global Optimization,December,1997(11):341–359.
[136] Price K.V. chapter Differential Evolution, New Ideas in Optimization[M].London, McGraw-Hill,1999.
[137] Musso, C., Oudjane, N., and Legland, F.:‘Improving regularised particle filter’,in Doucet, A., de Freitas, J.F.G., Gordon, N.J.(Eds):‘Sequential Monte Carlomethods’(Springer-Verlag,2001), pp.247–272
[138] Casella G., Robert C. P. Rao-Blackwellisation of samplings chemes[J].Biometrika,1996,83(1):81–94
[139] De Freitas, J. F. G., Niranjan, M., Gee, A. H., and Doucet, A. Sequential MonteCarlo methods to train neural network models[J], Neural Comput.,2000,12(4):955–993.
[140] Merwe, R., Doucet, A., Freitas, N., and Wan, E.:‘The unscented particle filter’,Technical report CUED/F-INFENG/TR380, Cambridge University,2000:1–40.
[141]袁泽剑,郑南宁,贾新春.高斯–厄米特粒子滤波器[J].电子学报,2003,31(7):970–973.
[142]梁军利,杨树元,曲超,高丽.一种新的基于数值积分的粒子滤波算法[J].电子与信息学报,2007,29(6):1369–1372.
[143] Julier S. J., Uhlmann J. K. A general method for approximating nonlineartransformations of probability distributions[R]. Dept. of Engineering Science,University of Oxford,1996.
[144] Julier S. J. The scaled unscented transformation[C]. Proceedings of the AmericanControl Conference,2002:4555–4559.
[145] Jazwinski, A.:‘Stochastic processes and filtering theory’(New York: AcademicPress,1970)
[146] Wan, E., and Van der Merwe, R. The unscented Kalman filter for nonlinearestimation[J]. In Proceedings of Adaptive Systems for Signal Processing,Communications, and Control Symposium,2000:53–158.
[147] Skolnik, M.I. Radar Handbook[M]. The McGraw-Hill Co. and Publishing Houseof Electronics Industry Press,2nd edition,2003:726–734.
[148] Hewer, G., Martin, R., and Zeh, J. Robust preprocessing for Kalman filtering ofglint noise[J]. IEEE Trans. Aerosp. Electron. Syst.,1987,23:120–128.
[149] Borden, B., Mumford, M.A. Statistical glint/radar cross section target model[J].IEEE Trans. Aerosp. Electron. Syst.,1983, AES-19,(1):781–785.
[150] Wu, W., and Cheng, P. A nonlinear IMM algorithm for maneuvering targettracking[J]. IEEE Trans. Aerosp. Electron. Syst.,1994,30,(3):875–884.
[151] Blom, H., and Bar-Shalom, Y. The interacting multiple model algorithm withMarkovian switching coefficients[J]. IEEE Trans. Automatic Control.1988,33,(8):780–783.
[152] Mazor, E., Averbuch, A., Bar-Shalom, Y., and Dayan, J. Interacting multiplemodel methods in target tracking: A survey[J]. IEEE Trans. Aerosp. Electron.Syst.,1998,34,(1):103–123
[153] Boers, Y., and Driessen, J.N. Interacting multiple model particle filter[J]. IEEProc., Radar Sonar Navig.,2003,150,(5):344–349.
[154] Guo Ronghua, Qin Zheng, and Li Xiangnan, et al. Interacting multiple modelparticle-type filtering approaches to ground target tracking[J]. Journal ofComputers,2008,3,(7):23–30.
[155] Wang Da-bao, Liu Shang-qian, Wang Hui-feng. Mono-station passive locationalgorithm based on particle filter and interacting multiple model[J]. SignalProcessing (Chinese),2008,25,(10):1566–1568.
[156] Petsios, M.N., Alivizatos, E.G., and Uzunoglu, N.K. Manoeuvring target trackingusing multiple bistatic range and range-rate measurements[J], Signal Processing,2007,87:665–686.
[157] Cardinali, R., Colone, F., Lombardo, P., Crognale, O., and Cosmi, A. Mutipathcancellation on reference antenna for passive radar which exploits FMtransmission[J]. IET International Radar Conference, Edinburgh, UK, October2007:15–18.
[158] Zhao Hong-li, Wu Tie-ping, Bao Zheng. The method for improving the rangeresolution in a passive location system[J]. Journal of Xidian University,2006,33,(2):165–168.
[159] Bongioanni, C., Colone, F., Lombardo, P. Performance Analysis of aMulti-Frequency FM Based Passive Bistatic Radar[J]. IEEE Radar Conference,May2008:1–6.
[160] Christian R. B., Shengli Zhou and Peter W. Signal extraction using compressedsensing for passive radar with OFDM signals[C].11thInternational Conferenceon Information Fusion2008:1–6
[161] Blom, H. A., Bloem, E. A. Exact Bayesian and particle filtering of stochastichybrid systems[J]. IEEE Trans. Aerosp. Electron. Syst.,2007,43,(1):55–70.
[162] Blom, H. A. P. An efficient filter for abruptly changing systems[J]. InProceedings of the23rd IEEE Conference on Decision and Control, Las Vegas,NV, Dec.1984.
[163] Foo, P. H., and Ng, G. W. Combining the interacting multiple model method withparticle filters for manoeuvring target tracking[J]. IET Radar Sonar Navig.,2011,5,(3):234–255.
[164] Doucet, A. and Ristic, B.:’Recursive state estimation for multiple switchingmodels with unknown tansition probabilities.’ IEEE Trans. Aerosp. Electron.Syst.,2002,38,(3), pp.1098–1104