粒子初始化方法在雷达目标跟踪中应用

粒子滤波威廉希尔官方网站 通过非参数化的蒙特卡罗interwetten与威廉的赔率体系 方法实现递推贝叶斯滤波，适用于非线性
目标运动模型、非高斯噪声的目标跟踪。在实际应用中，初始粒子选取的精度对滤波效果往往会造成很大的影响；本文将竞争策略应用于初始粒子的选取，通过初始观测值生成若干粒子群，并在滤波初始阶段统计各粒子群中的有效粒子数量，最后选择有效粒子数量最多的粒子群构造初始化粒子。仿真结果表明，本算法可以提高初始粒子选取的精度，从而改善滤波效果。
关键词：粒子滤波；初始化；目标跟踪
Abstract：Particle filter realizes recursive Bayes filter through non-parameter Monte Carlo
technique, it can be applied to target tracking in nonlinear and non-Gaussian environment. In
practice, the accuracy of initial particles will affect the result of filter heavily. In this paper, initial particles comes out through competition-cooperation strategy, that is, initial measurements give birth to several particle groups, each of the groups is tested separately in the beginning, the group with the most number of efficient particles is selected as the initial particles. Results of simulation show that the method improves the accuracy of initial particles, which makes the effect of filter much better.
Key words: Particle Filter; Initiation; Target Tracking