未知雜波條件下樣本集校正的勢(shì)估計(jì)概率假設(shè)密度濾波算法
doi: 10.11999/JEIT170666 cstr: 32379.14.JEIT170666
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61372003, 61503293)
A Cardinalized Probability Hypothesis Density Filter with Unknown Clutter Estimation Using Corrected Sample Set
Funds:
The National Natural Science Foundation of China (61372003, 61503293)
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摘要: 在貝葉斯框架下的多目標(biāo)跟蹤算法中，總是假設(shè)雜波的先驗(yàn)信息是已知的。然而，實(shí)際應(yīng)用中，雜波分布一般是未知的，假設(shè)的雜波分布往往與實(shí)際情況匹配度差，難以保證濾波精度。針對(duì)該問題，該文研究了未知雜波勢(shì)估計(jì)概率假設(shè)密度(CPHD)濾波算法。首先，提出一種基于狄利克雷過程混合模型(DPMM)類的未知雜波CPHD算法，該算法能夠自動(dòng)選取合適的類數(shù)對(duì)雜波進(jìn)行描述，有效降低了雜波空間分布估計(jì)的誤差。此外，提出樣本集校正的思想，并將其引入所提算法，通過去除樣本集中由真實(shí)目標(biāo)產(chǎn)生的量測(cè)，較好地解決了雜波數(shù)過估和目標(biāo)數(shù)低估的問題。與傳統(tǒng)算法相比，所提算法的濾波精度更接近于雜波信息匹配情況下的性能，仿真結(jié)果驗(yàn)證了其優(yōu)越性與魯棒性。
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關(guān)鍵詞:
- 多目標(biāo)跟蹤 /
- 參數(shù)估計(jì) /
- 未知雜波 /
- 狄利克雷過程混合模型 /
- 勢(shì)估計(jì)概率假設(shè)密度濾波
Abstract: In multi-target tracking algorithms under the Bayesian filtering framework, it is usually assumed that the priori knowledge of clutter is known. However, in practice, the knowledge of clutter is usually unknown, and the assumption of clutter may not agree with the truth, resulting in the filtering precision declining. For this problem, this paper addresses the problem of Cardinalized Probability Hypothesis Density (CPHD) filter with clutter estimation. Firstly, this paper presents a new CPHD filter with clutter estimation based on Dirichlet Process Mixture Model (DPMM). Thus, this DPMM--CPHD algorithm can reduce the estimation error of the clutter spatial distribution effectively by selecting an appropriate class number. Secondly, to solve the clutter overestimation and cardinality underestimation problems, a correction idea of the sample set via CPHD filter recursion is proposed. By introducing this idea to the DPMM--CPHD algorithm, an improved DPMM--CPHD algorithm is proposed to solve this intractability of errors on clutter number and target number. Simulation results show that the proposed algorithm can effectively estimate the unknown parameters of clutter and has a good performance of multi-target tracking. -
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