Cardinalized Probability Hypothesis Density Filter Based on Pairwise Markov Chains
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Electronic and optical engineering Department, Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, China
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摘要:
針對已有的基于雙馬爾科夫鏈(PMC)模型的勢概率假設(shè)密度(PMC-CPHD)濾波算法無法實現(xiàn)的問題，將PMC-CPHD算法改進為多項式形式以便于算法的實現(xiàn)，并給出了改進算法的高斯混合(GM)實現(xiàn)。實驗結(jié)果表明給出的GM實現(xiàn)能夠有效實現(xiàn)多目標跟蹤，并且比基于PMC模型的概率假設(shè)密度(PMC-PHD)算法的GM實現(xiàn)提高了目標個數(shù)估計的穩(wěn)定性。
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關(guān)鍵詞:
- 雙馬爾科夫鏈 /
- 勢概率假設(shè)密度 /
- 高斯混合
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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