改进高斯混合粒子滤波的纯方位目标跟踪算法

An Improved Gaussian Mixture Particle Filter Based Targets Tracking Algorithm for Bearing-Only Tracking System

针对纯方位跟踪系统非线性较强、传统跟踪滤波方法收敛速度慢且容易发散的问题,提出了一种基于改进高斯混合粒子滤波的纯方位跟踪算法。该算法基于Sigma点卡尔曼滤波(SPKF)和粒子滤波的特点,用有限的高斯混合模型来近似后验状态密度、系统噪声和观测噪声的分布。利用贪心EM算法实现模型的降阶,一定程度上克服了EM算法假定混合成分数为已知、迭代的结果需要依赖初始值、可能收敛到局部最大点和可能收敛到参数空间的边界的缺点,从而改善粒子枯竭的问题。仿真实验结果表明在纯方位跟踪领域,与传统粒子滤波(PF)和基于EM的高斯混合粒子滤波相比,该算法在保持高精度估计能力的同时,具有较强的鲁棒性,是解决非线性系统状态估计问题的一种有效方法。

An improved Gaussian mixture particle filter algorithm is proposed for the highly non-linear bearing-only tracking system where the common tracking filters often fail to catch and keep tracking of the emilter. In the algorithm, based on the characteristics of SPKF and particle filter, the limited Gaussian mixture model is used to approximate the posterior density of states, system noise and measurement noise. The greedy EM is used to obtain the reduced order model and overcome such disadvantages of the standard EM as the number of the mixture components is assumed a known a priori, the performance of the overall parameter estimation process depends on the given good initial settings, and the estimated parameter can be resulted from some local optimum points, thus lessening effects caused by sampling; depletion. Simulation results show that the algorithm outperforms the one based on PF and the one based on EM-GMPF in tracking accuracy and stability. Therefore it is more suitable to the nonlinear state estimation.