基于CPFDE的目标跟踪算法研究

在目标跟踪中,为了克服粒子滤波的粒子退化和贫化问题,提高滤波精度,文中将差分演化算法与容积粒子滤波相结合,形成了差分演化容积粒子滤波算法。在粒子进行先验更新时, 使用容积卡尔曼滤波算法融入当前时刻的量测信息并用其来产生重要性密度函数,并且在重采样阶段,用差分演化算法对根据重要性密度函数抽取的采样粒子做优化操作,从而克服粒子滤波存在的粒子退化及贫化问题,提高滤波性能。实验结果表明,和粒子滤波、无迹粒子滤波、容积粒子滤波相比,该算法有着更高的滤波精度和更好的稳定性,并且能够提高雷达机动目标跟踪的精确性。

Target Tracking Based on Cubature Particle Filter Differential Evolution Algorithm

In order to overcome the problem of particle degradation and depletion in particle filter for target tracking, this paper combines differential evolution algorithm with cubature particle filter to form a differential evolution cubature particle filter algorithm. The latest observation data is integrated into cubature Kalman filter ( CKF) to produce the importance density function,when the particle prior distribution is updated; meanwhile, in the resampling stage, the differential evolution algorithm is used to optimize the sampling particles caused by the importance distribution of CKF. Therefore, the particle degradation and depletion in particle filter are overcome and the filter performance is improved. Simulation results show that this algorithm has higher accuracy and better stability than particle filter, unscented particle filter and cubature particle filter. And this algorithm can improve the accuracy of radar maneuvering target tracking.