基于箱粒子的多扩展目标PHD滤波

PHD filter for tracking multiple extended targets using box particle

在高斯混合多扩展目标PHD滤波的基础上,结合最新兴起的箱粒子滤波,提出一种基于区间分析的多扩展目标PHD滤波算法.采用大小可控的非零矩形区域来代替传统的多个点量测,这样可降低权值计算中对量测分布的要求.仿真对比实验表明,采用区间分析方法在保证近似于传统滤波精度的同时可降低计算复杂度,在目标数目估计及抗杂波干扰方面也具有较为突出的优势,并且可解决在目标靠近时由于不能正确给出子划分而造成的漏检问题.

A novel probability hypothesis density（PHD） filter for tracking multiple extended targets is proposed by using interval analysis resulting from the Gaussian mixture PHD（GM-PHD） and the recently emerged box particle filtering. The key idea is replacing traditional multiple measurements with a rectangular region of the non-zero volume in the state space,which can reduce the requirement of the measurements＇ distribution. Simulation results show that, using interval analysis can reach the same tracking level of GM-PHD with a low computational complexity and a good performance on estimating the number of the targets and anti-clutter. This approach can also solve the problem of leak detection with the wrong subpartition.