基于隨機攝動再采樣的粒子概率假設(shè)密度濾波器

徐從安; 何友; 夏沭濤; 程俊圖; 董云龍

doi:10.11999/JEIT160114

基于隨機攝動再采樣的粒子概率假設(shè)密度濾波器

doi: 10.11999/JEIT160114

1.
(海軍航空工程學院信息融合研究所煙臺 264001) ②(中國人民解放軍91213部隊煙臺 264000)

基金項目:

國家自然科學基金 (61471383, 61304103)

計量
- 文章訪問數(shù): 1396
- HTML全文瀏覽量: 110
- PDF下載量: 417
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-01-26
- 修回日期: 2016-07-08
- 刊出日期: 2016-11-19

Particle Probability Hypothesis Density Filter Based on Stochastic Perturbation Re-sampling

2.
(Unit. 91213 of PLA, Yantai 264000, China)

Funds:

The National Natural Science Foundation of China (61471383, 61304103)

摘要

摘要: 作為概率假設(shè)密度濾波的典型實現(xiàn)方式，粒子概率假設(shè)密度濾波器無需線性高斯等先驗假設(shè)，因而在多目標跟蹤中得到了廣泛的應用。為解決粒子退化問題并保持粒子規(guī)模，該濾波器引入了重采樣機制，然而，該重采樣機制易引起粒子多樣性耗盡，導致粒子貧化問題產(chǎn)生。為解決這一問題，該文提出一種新的基于隨機攝動再采樣的粒子概率假設(shè)密度濾波器。首先，全面分析了粒子概率假設(shè)密度濾波因粒子貧化問題導致目標失跟的過程。然后設(shè)計了一種隨機攝動再采樣算法，該算法在重采樣導致粒子多樣性缺失時，根據(jù)源粒子的位置與復制次數(shù)隨機產(chǎn)生相應數(shù)目的新粒子，并對源粒子進行刪減，其可在保留源粒子信息的前提下保持粒子的多樣性。最后，該文將該算法納入概率假設(shè)密度濾波框架，提出了一種新的粒子概率假設(shè)密度濾波器。仿真結(jié)果表明該濾波器在不顯著增加運行時間的前提下能夠克服粒子貧化問題，相比標準的粒子概率假設(shè)密度濾波器具有更好的跟蹤性能。
- 多目標跟蹤 /
- 概率假設(shè)密度 /
- 粒子濾波 /
- 隨機攝動再采樣
Abstract: As a typical implementation of the Probability Hypothesis Density (PHD) filter, Particle PHD (P-PHD) is suitable for highly nonlinear systems and widely used in Multi-Target Tracking (MTT). However, the resampling in P-PHD filter, recommended to avoid particle degeneracy, introduces the problem of diversity loss among the particles, namely particle impoverishment problem. To solve the problem and improve the performance of the P-PHD filter, a novel filter based on stochastic perturbation re-sampling is proposed. First, a comprehensive analysis on the particle impoverishment problem of P-PHD filter is presented. Then for the purpose of keeping the particle diversity, a new stochastic perturbation re-sampling algorithm is developed, which generates new particles according to the position and duplicating times of the original particles, and removes some excessive copied particles. Finally, the re-sampling algorithm is integrated into the P-PHD filter framework and a Stochastic Perturbation Particle PHD (SPP-PHD) filter is proposed. Numerical examples show that the proposed filter can overcome the particle impoverishment problem and improve the estimation performance on the premise of not significantly improving the simulation time.
- Multi-Target Tracking (MTT) /
- Probability Hypothesis Density (PHD) /
- Particle Filter (PF) /
- Stochastic perturbation re-sampling

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