被动传感器阵列中基于粒子滤波的目标跟踪

针对被动传感器阵列中的机动目标跟踪问题，该文提出了一种基于多模Rao-Blackwellized粒子滤波的机动目标跟踪新方法。算法首先基于Rao-Blackwellization理论将机动目标跟踪问题划分为模型选择和目标跟踪两个子问题；采用多模Rao-Blackwellized粒子滤波对目标运动模型进行选择，扩展Kalman滤波对目标进行更新，有效降低了抽样粒子状态维数，节省了计算时间；最后，建立了被动传感器阵列的非线性观测模型。实验结果表明，提出方法可以有效地对目标模型进行选择，算法的跟踪性能及稳定性要好于交互多模型(IMM)方法。

Target Tracking Based on Particle Filtering in Passive Sensor Array

In this paper, a new Multiple Model Rao-Blackwellized Particle Filter (MMRBPF) based algorithm is proposed for maneuvering target tracking in passive sensor array. The advantage of the proposed approach is that the Rao-Blackwellization allows the algorithm to be partitioned into target tracking and model selection sub-problems, where the target tracking can be solved by the extend Kalman filter, and the model selection by multiple model Rao-Blackwellized particle filter. The analytical relationship between target state and model is exploited to improve the efficiency and accuracy of the proposed algorithm. Finally, a nonlinear measurement model of multiple passive sensors is founded. The simulation results show that the proposed algorithm results in more accurate tracking than the IMM (Interacting Multiple Model) method.