基于双马尔可夫链的概率假设密度滤波器

隐式马尔可夫链(hidden Markov chain,HMC)是传统多目标跟踪的理论基础。在分析了HMC模型的局限性基础上,介绍了更具普适性的双马尔可夫链(pairwise Markov chain,PMC)模型,对基于PMC模型的概率假设密度(PMC-probability hypothesis density,PMC-PHD)滤波算法进行了推导,并对其高斯混合(Gauss-mixture,GM)实现进行了改进,利用椭圆波门给每一个高斯分量建立一个对应的缩减量测集合来对其进行更新。仿真实验证明在杂波密度较大的场景中,PMC-PHD滤波器GM实现的改进在不影响跟踪精度的情况下运行时间缩短为原来的三分之一;仿真实验还证明在HMC模型场景下PMC-PHD滤波器针对邻近目标的跟踪性能要优于HMC-PHD滤波器。

Probability hypothesis density filter based on pairwise Markov chains

Hidden Markov chain (HMC) is the theoretical basis of traditional multi-target tracking. Based on the analysis of the limitations of HMC model, the more general pairwise Markov chain (PMC) model is introduced, and probability hypothesis density (PHD) based on PMC model filtering algorithm is deduced, and its Gauss mixture (GM) implementation is improved by using an elliptic gate to establish a reduced measurement set for each Gaussian component to update corresponding Gaussian component. The simulation experiment shows that the improved GM implementation of PMC-PHD filter achieves 1/3 of the original time without affecting the tracking precision in the scenario of high clutter density, and the simulation experiment also proves that the tracking performance of PMC-PHD filter for the adjacent targets in the HMC model scene is better than HMC-PHD filter.