两站无源定位系统中的多目标跟踪算法研究

两站多目标纯方位跟踪中的虚假定位点识别问题是被动定位中尚待解决的一个重要问题,本文对此问题进行了研究,并给出了一种排除两站虚假定位点的新方法.该方法首先根据两个观测站在初始时刻所测得的角度进行交叉定位,由定位结果估计目标初始时刻所在的空间范围,并对此空间范围进行分区;然后对方位数据进行关联,以判断哪些方位角数据可能来自同一个目标;在每一个子区间上分别利用扩展卡尔曼滤波模型(EKF)进行滤波,并给出目标落在每个子区间上的概率,把此概率值作为权重对各个子区间的滤波结果进行加权来估计目标的状态;该概率值在以后的跟踪过程中按照贝叶斯准则不断进行更新,而更新概率低于检测门限的子区间予以取消.通过仿真分析可看出,利用本文提出的方法两个观测站可对多目标进行良好的定位和跟踪.

Study on Multitarget Tracking Algorithm in Two Direction-Finding Location Systems

Recognition of ghosts is a very important unsolved problem in two direction-finding location systems. This paper studies the problem and proposes a new method to solve it. Firstly, the bearing measurements of two passive sensors are used to estimate the initial range intervals of targets that are divided into a number of subintervals. Then, the bearing measurements are associated to judge which measurements belong to the same target. An extended Kalman filter ( EKF) is designed for each subinterval. And initial probability of target lying in each subinterval is also given. The combined state estimate is obtained as weighted sums of the state estimates of each subinterval.This probability is calculated recursively according to Bayes' rule in the tracking. If the probability is below a detection threshold this model is abolished. Simulation results show that by using the algorithm discussed in this paper two passive sensors can locate and track multiple targets at the same time.