Real-Time Marginalized Particle Filter Based on Weights Consistency Optimization

HU Zhen-tao; LIU Xian-xing; JIN Yong; HOU Yan-dong

doi:10.3969/j.issn.0372-2112.2014.10.016

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Real-Time Marginalized Particle Filter Based on Weights Consistency Optimization

更新时间：2025-07-16

- Real-Time Marginalized Particle Filter Based on Weights Consistency Optimization
- Acta Electronica Sinica Vol. 42, Issue 10, Pages: 1970-1976(2014)
- 作者机构：
  
  河南大学图像处理与模式识别研究所,河南,开封,475004
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61300214, No.U1204611, No.61374134);Science and Technology Innovation Team Support Plan of Colleges and Universities in Henan Province (No.13IRTSTHN021);Henan Research Program of Basic and Frontier Technology (No.132300410148);Key Project of Science and Technology Research of Education Department of Henan Province (No.13A413066);Young Backbone Teachers Funding Project of Henan Province (No.2010GGJS-041)
- DOI：10.3969/j.issn.0372-2112.2014.10.016
  CLC： TP391
- Published：2014
- 稿件说明：
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HU Zhen-tao, LIU Xian-xing, JIN Yong, et al. Real-Time Marginalized Particle Filter Based on Weights Consistency Optimization[J]. Acta Electronica Sinica, 2014, 42(10): 1970-1976.
DOI：

HU Zhen-tao, LIU Xian-xing, JIN Yong, et al. Real-Time Marginalized Particle Filter Based on Weights Consistency Optimization[J]. Acta Electronica Sinica, 2014, 42(10): 1970-1976. DOI： 10.3969/j.issn.0372-2112.2014.10.016.

摘要

针对Marginalized粒子滤波中随机量测噪声对于非线性状态估计精度的不利影响以及线性状态估计中计算量较大问题

提出了一种基于权重一致性优化的实时Marginalized粒子滤波算法.首先

结合量测系统建模中先验信息的提取和利用

通过粒子权重间一致性距离和一致性矩阵的构建

提出了量测提升策略下权重的一致性优化方法

以改善粒子滤波在非线性状态估计中的滤波精度.其次

通过对Marginalized粒子滤波实现中时间更新和量测更新环节的结构优化

给出了实时Marginalized粒子滤波

以降低蒙特卡罗仿真实现下卡尔曼滤波在状态线性估计中的计算复杂度.最后

在两者的动态结合基础上给出了新算法具体实现步骤.利用基于单站雷达目标跟踪仿真场景

分析了算法性能.理论分析和仿真实验结果验证了算法的可行性和有效性.

Abstract

Aiming to adverse influence on the filtering precision of nonlinear state estimation caused by the random observation noise and the improvement of larger calculated amount from linear state estimation in marginalized particle filter

a novel real-time marginalized particle filter based on weights consistency optimization is proposed.Firstly

according to the extraction and utilization of prior information from observation system model

the consistency optimization method of particle weights in observation lifting scheme is given by the construction of consistency distance and consistency matrix

which improves the filtering precision of particle filter used in nonlinear state estimation.Secondly

the real-time marginalized particle filter is proposed by the structure optimization of time update and observation update steps

which decrease the computational complexity of Kalman filter used in the linear state estimation in view of Monte Carlo simulation principle.Finally

the concrete steps of new algorithm are given by the dynamic combination of the consistency optimization method and the real-time marginalized particle filter.The filtering precision and calculated amount of new algorithm is analyzed on the basis of single station radar observation target tracking simulation scene.The theoretical analysis and experimental results show the feasibility and efficiency of algorithm proposed.

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