摘要
该文对标准型SMC-PHD滤波器作了两点改进。第一,提出基于观测值的目标个数和目标状态估计方法,该方法首先计算以观测值为行、存活粒子为列的权值矩阵,将按行计算的权值和与判决门限比较,把大于门限的观测值判决为真实观测值,并据此估算目标个数和目标状态。第二,为每个粒子分配表示存活年龄的辅助变量,以抑制强杂波环境下的目标数高估问题。仿真实验表明,在强杂波环境下,增强型SMC-PHD算法在多目标跟踪稳定性方面优于标准型SMC-PHD算法。
Two improved contributions have been advanced for the standard Sequential Monte Carlo Probability Hypothesis Density （SMC-PHD） filter. Firstly, a novel method is advanced for the cardinality and state estimation. A weight matrix is firstly calculated by measurements and persistent particles, and the weight sum of each row is then evaluated, the measurements indexed by row will be judged as true if its weight sum is larger than a certain threshold, and the weight sum of persistent particle states will be reported as the true target states. Secondly, an assistant variable which is used to denote the persistent age for every particle is introduced, by the help of this age variable, the overrated problem of targets number in dense clutter environment can be effectively restricted. The results of numerical simulation prove that the improved SMC-PHD filter has higher tracking performance than the standard one.
基金
安徽省自然科学基金(090412067)资助课题
关键词
多目标跟踪
概率假设密度
序贯蒙特卡罗
目标出生强度
Multiple target tracking
Probability Hypothesis Density （PHD）
Sequential Monte Carlo （SMC）
Target birth intensity
