摘要
为提高非视距情况下移动辐射源的定位精度,提出一种改进的交互粒子滤波算法.该算法对目标运动多模型和信号到达时间差测量噪声分布多模型联合建模.在交互多模型状态更新中利用粒子滤波对目标时变状态以及视距/非视距混合信道参数进行估计,抑制了非视距测量误差对移动定位的影响.仿真结果表明,改进算法的性能要优于现有的视距条件运动多模型和视距/非视距条件单一运动模型的定位算法,并且定位误差接近于推导的后验克拉美劳下界.
To improve the positioning accuracy of a mobile transmitter under the non-line-of-sight( NLOS) condition,an enhanced interacting particle filtering algorithm is proposed. The multiple motion models of the target and the multiple measurement noise distribution models of the time difference of arrival( TDOA) of the target signal are jointly built. In the state update phase of the interacting multiple models, the particle filtering is utilized to estimate the time-varying state of the target and the line-of-sight( LOS)/NLOS mixed channel parameters, thus the effect of NLOS measurement errors on mobile localization can be eliminated. Simulation results demonstrate that the proposed method performs better than the existing multiple motion model positioning method under the LOS condition and single motion model positioning method under the LOS/NLOS condition, and is close to the derived posterior Cramer-Rao lower bound.
引文
[1]Wang G,Chen H,Li Y,Ansari N. NLOS error mitigation for TOA-based localization via convex relaxation[J]. IEEE Transactions on Wireless Communications,2014,13(8):4119-4131.
[2]Yin F,Fritsche C,Gustafsson F,Zoubir A M. TOA-based robust w ireless geolocation and Cramer-Rao low er bound analysis in harsh LOS/NLOS environments[J]. IEEE Transactions on Signal Processing,2013,61(9):2243
[3] Hammes U,Wolsztynski E,Zoubir A M. Robust tracking and geolocation for w ireless netw orks in NLOS environments[J]. IEEE Journal of Selected Topics in Signal Processing,2009,3(5):889-901.
[4] Mc Guire M,Plataniotis K N,Venetsanopoulos A N. Data fusion of pow er and time measurements for mobile terminal location[J]. IEEE Transactions on M obile Computing,2005,4(2):142-153.
[5] Liao J,Chen B. Robust mobile location estimator with NLOS mitigation using interacting multiple model algorithm[J]. IEEE Transactions on Wireless Communications,2006,5(11):3002-3006.
[6]Yang C,Chen B,Liao F. Mobile location estimation using fuzzy based IM M and data fusion[J]. IEEE Transactions on M obile Computing,2010,9(10):1424-1436.
[7]Hammes U,Zoubir A M. Robust MT tracking based on Mestimation and interacting multiple model algorithm[J].IEEE Transactions on Signal Processing,2011,59(7):3398-3409.
[8]Xia N,Wei W,Li J,Zhang X. Kalman particle filtering algorithm for symmetric alpha-stable distribution signals w ith application to high frequency time difference of arrival geolocation[J]. IET Signal Processing,2016,10(6):619-625.
[9]Djuric P M,Kotecha J H,Zhang J,Huang Y,Ghirmai T,Bugallo M F,M uguez J. Particle filtering[J]. IEEE Signal Processing M agazine,2003,20(5):19-38.
[10]夏楠,邱天爽,李景春,李书芳.一种卡尔曼滤波与粒子滤波相结合的非线性滤波算法[J].电子学报,2013,41(1):148-152.Xia Nan,Qiu Tian-shuang,Li Jing-chun,Li Shu-fang. A nonlinear filtering algorithm combining the Kalman filter and the particle filter[J]. Acta Electronica Sinica,2013,41(1):148-152.(in Chinese)
[11] Chang D,Fang M. Bearing-only maneuvering mobile tracking w ith nonlinear filtering algorithms in w ireless sensor netw orks[J]. IEEE Systems Journal,2014,8(1):160-170.
[12] Foo P H,Ng G W. Combining the interacting multiple model method w ith particle filters for manoeuvring target tracking[J]. IET Radar Sonar Navigation,2011,5(3):234-255.
[13] Zuo L,Niu R,Varshney P K. Conditional posterior Camer-Rao Low er bounds for nonlinear sequential Bayesian estimation[J]. IEEE Transactions on Signal Processing,2011,59(1):1-14.
