基于相对熵的概率假设密度滤波器序贯蒙特卡罗实现方式

概率假设密度滤波器的典型序贯蒙特卡罗实现方式与粒子滤波类似,均是利用大量加权粒子估计多目标状态,典型实现方式是为每个期望目标分配固定数目的粒子,这导致较大的算法时间开销.鉴于此,建立了基于相对熵的序贯蒙特卡罗实现方式.首先计算两个不同规模粒子集合的相对熵,与预设阈值进行比较以确定粒子数目,从而动态调整粒子数目.仿真结果表明,所提出的实现方式提高了跟踪效率,在大部分时间步上优于典型实现方式.

Sequential Monte Carlo implementation of PHD filter based on Kullback-Leibler divergence

The typical sequential Monte Carlo(SMC) implementation of probability hypothesis density(PHD) filter is similar with the particle filter. Both of them make use of a large number of particles to estimate the multiple target states. The fixed number of particles is assigned for each expected target in typical SMC implementation, which will result in larger time cost of the algorithm. Therefore, the SMC implementation based on Kullerback-Leibler divergence(KLD) is proposed. The KLD is computed for the two particle sets in different sizes. Then, the KLD is compared with the pre-threshold to obtain the number. The number of particles can be adaptively adjusted in the proposed implementation. Simulation results show that, the proposed implementation can improve the tracking efficiency, which is superior to the typical implementation in most time steps.