基于联合TDOA/FDOA的单站无源相干定位CWLS算法
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摘要
针对利用单站实现对运动目标位置和速度的估计精度不够理想的问题,提出了一种基于联合TDOA/FDOA的单站无源相干定位(PCL)约束加权最小二乘(CWLS)算法。首先根据运动目标、观测接收站与机会照射源三者之间构建的联合TDOA/FDOA方程,建立伪线性方程并将其线性化,然后根据目标到观测接收站的距离与目标位置之间的函数约束关系,建立为CWLS定位模型,并采用迭代方法进行求解。仿真实验和结果分析表明,在存在测量误差的情况下,该算法仍然能够逼近克拉美罗界(CRLB)。
To solve the target location's estimation accuracy is not good enough for the single-observer passive coherent location(PCL) using illuminators of opportunity,a constrained weighted least squares(CWLS) location algorithm based on time differences and frequency differences of arrival is proposed. Firstly,introducing the range from the target to the observer and illuminators of opportunity as the additional variable,the TDOA and FDOA measurement equations are linearized. Considering the relationship between the additional variable and the target location,the location problem is established as a CWLS model. To solve the CWLS model to obtain the estimation of the target position and velocity,the solution is derived based on iterative method. Finally,the theoretical error and the Cramer Rao Lower Bound(CRLB) are also derived. Simulation results show that the proposed algorithm approaches the CRLB at high noise level.
To solve the target location's estimation accuracy is not good enough for the single-observer passive coherent location(PCL) using illuminators of opportunity,a constrained weighted least squares(CWLS) location algorithm based on time differences and frequency differences of arrival is proposed. Firstly,introducing the range from the target to the observer and illuminators of opportunity as the additional variable,the TDOA and FDOA measurement equations are linearized. Considering the relationship between the additional variable and the target location,the location problem is established as a CWLS model. To solve the CWLS model to obtain the estimation of the target position and velocity,the solution is derived based on iterative method. Finally,the theoretical error and the Cramer Rao Lower Bound(CRLB) are also derived. Simulation results show that the proposed algorithm approaches the CRLB at high noise level.
引文
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[14] GUO F C,HO K C. A Quadratic Constraint Solution Method for TDOA and FDOA Localization[J]. International Conference on Acoustics Speech and Signal Processing,Prague,Czech,2011,125(3):2588-2591.
[15] KAY S M. Fundamentals of Statistical Signal Processing:Estimation Theory[M]. Englewood Cliffs,NJ:Prentice-Hall,
[2]杨进佩,刘中,朱晓华.用于无源雷达的GPS卫星信号性能分析[J].电子与信息学报,2007,29(5):1083-1086.
[3]周明章,姚广涛,孙海信,等. Wi Fi信号机会照射源雷达双目标检测的研究[J].舰船电子对抗,2016,39(2):1-4.
[4] MICHAEL E,ALEXANDER S,FABIENNE M. Design And Performance Evaluation of a Mature FM/DAB/DVB-T Multi-Illuminator Passive Radar System[J]. IET Radar,Sonar and Navigation,2014,8(2):114-122.
[5]郭福成,李腾,冯道旺,等.基于运动学原理的地对空单站被动跟踪研究及试验[J].火控雷达技术,2006,35(2):5-8.
[6]陈健,卓永宁.一种基于TOA的定位优化算法[J].无线电通信技术,2010,36(4):52-54.
[7] LIN L,SO H C,CHAN F K W,et al. A New Constrained Weighted Least Squares Algorithm for TDOA-Based Localization[J]. Signal Processing,2013,93(11):2872-2878.
[8]唐涛,栾鹏程,吴瑛.基于DOA的无源定位算法[J].新能源进展,2008,13(3):140-144.
[9]李金洲,郭福成.传感器位置误差条件下仅用到达频率差的无源定位性能分析[J].航空学报,2011,32(8):1497-1505.
[10] HO K C,XU W. An Accurate Algebraic Solution For Moving Source Location Using TDOA and FDOA Measurements[J]. IEEE Transactions on Signal Processing,2004,52(9):2453-2463.
[11]朱华进,张洋,鄂嵋,等.基于约束加权最小二乘的时差频差联合定位算法[J].电讯技术,2013(4):395-401.
[12] HE Y,ZHANG C S,TANG X M,et al. Coherent Integration Loss Due to Pulses Loss And Phase Modulation in Passive Bistatic Radar[J]. Digital Signal Processing,2013,23(4):1265-1276.
[13] HUANG Y,BENESTY J,ELKO G W,et al. Realtime Passive Source Localization:a Practical Linear-Correction Least-Squares Approach[J]. IEEE Transactions on Speech&Audio Processing,2001,9(8):943-956.
[14] GUO F C,HO K C. A Quadratic Constraint Solution Method for TDOA and FDOA Localization[J]. International Conference on Acoustics Speech and Signal Processing,Prague,Czech,2011,125(3):2588-2591.
[15] KAY S M. Fundamentals of Statistical Signal Processing:Estimation Theory[M]. Englewood Cliffs,NJ:Prentice-Hall,