杂波环境下被动多传感器机动目标跟踪新算法

针对杂波环境被动传感器机动目标跟踪问题，该文研究了一种基于粒子滤波的被动多传感器机动目标跟踪新算法。 在该算法中，首先推导了杂波环境下粒子滤波的似然函数表达式。其次将粒子滤波与交互多模型(IMM)相结合，用IMM方法实现模型的切换，以适应目标的机动变化。用粒子滤波实现对观测方程的非线性处理。最后，建立了被动多传感器的非线性观测模型，避免了目标的不可观测性，并且算法还能够处理非高斯噪声情况。仿真实验结果表明，提出的算法能够有效地对被动机动目标跟踪，且性能优于交互多模型概率数据关联滤波器(IMM-PDAF)。

Maneuvering Target Tracking Algorithm with Multiple Passive Sensors in Clutter Environment

To maneuvering target tracking with multiple passive sensors in clutter environment, a novel algorithm based on particle filter is proposed. In this algorithm, the likelihood function of particle filter in clutter is derived. Then the particle filter and Interactive Multiple Model (IMM) method are integrated. The former solves the proplem of passive target manoeuvre and the latter deals with the nonlinear problem of the measurement equation. In order to avoid the unobservability problem of passive target tracking, a nonlinear measurement model of multiple passive sensors is founded, and the algorithm can deal with the case of non-gaussian noise. Finally, the simulation results show that the proposed algorithm is effective, and its performance is superiority over the interacting multiple model-probabilistic data association filter (IMM-PDAF).