Cardinalized Probability Hypothesis Density Filter Based on Pairwise Markov Chains
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摘要:
针对已有的基于双马尔科夫链(PMC)模型的势概率假设密度(PMC-CPHD)滤波算法无法实现的问题，将PMC-CPHD算法改进为多项式形式以便于算法的实现，并给出了改进算法的高斯混合(GM)实现。实验结果表明给出的GM实现能够有效实现多目标跟踪，并且比基于PMC模型的概率假设密度(PMC-PHD)算法的GM实现提高了目标个数估计的稳定性。
Abstract:In view of the problem that the Cardinalized Probability Hypothesis Density (CPHD) probability hypothesis density filtering algorithm based on the Pairwise Markov Chains (PMC) model (PMC-CPHD) is not suitable for implementation, the PMC-CPHD algorithm is modified into a polynomial form to facilitate implementation, and the Gauss Mixture (GM) implementation of the improved algorithm is given. The experimental results show that the given GM implementation realizes multitarget tracking effectively, and improves the stability of the target number estimation compared with the GM implementation of the probability hypothesis density filtering algorithm based on the PMC model (PMC-PHD).
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