非线性非高斯模型的改进粒子滤波算法

针对被动定位跟踪系统非线性强、传统跟踪滤波方法收敛速度慢且容易发散的问题，给出了一种用于纯方位目标跟踪的改进粒子滤波算法。该算法首先用有限的高斯混合模型来近似后验状态密度；其次针对随机噪声对粒子权值准确性的影响，给出了改进的变权平均似然函数。根据χ2检验，对每个粒子权值的更新，采取由多次观测值计算粒子似然函数并对其求变权平均和单一观测值求似然函数相结合的方式进行，既减小随机观测噪声对权值的影响也提高了算法实时性；最后利用基于退火机制的Aitken加速EM算法（A-DAEM）取代传统粒子滤波的再采样过程，克服了EM算法容易陷入局部最值的缺点，改善了粒子枯竭的问题。仿真实验结果表明该算法与变权平均似然函数粒子滤波（PF-ALDP）和基于EM的高斯混合粒子滤波(EM-GMPF)相比，具有高精度估计能力和较强的鲁棒性，是解决非线性系统状态估计问题的一种有效方法。 

Improvement Particle Filtering Algorithm for Nonlinear Non-Gaussian models

An improved particle filter algorithm is proposed for the highly non-linear passive location and tracking system in which the common tracking filters often faile to catch and keep tracking of the emitter. Firstly, the algorithm uses limite gaussian mixture model to approximate the posterior density of states. Secondly, in order to solve the problem in which the stochastic observation noise influence the accuracy of particle weights, an improved based on averaging likelihood functions with diverse proportion is proposed. According to χ2 testing, the new method combines multi-observations to compute the likelihood functions of each particle and then average them with single observation computes the likelihood functions of each particle to update particle weights. The method not only reduces the influence of the stochastic observation noise to particle weight but also improves real-time. Finally, the traditional process of particle filter resampling is replaced by the aitken-deterministic annealing expectation maximization (A-DAEM) algorithm, avoiding to a local maximum and reducing the effects cause by sampling depletion. Simulation results show that the algorithm outperforms the one based on PF-ALDP and the other based on EM GMPF in tracking accuracy and stability. Therefore it is more suitable to the nonlinear state estimation.