Bayesian Sequential Importance Quadrature Filter

ZHANG Hong-wei; ZHANG Xiao-hu; CAO Yong

doi:10.12263/DZXB.20210716

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Bayesian Sequential Importance Quadrature Filter

INTELLIGENT TIME-SPATIAL INFORMATION SERVICE TECHNOLOGY | 更新时间：2025-12-08

- Bayesian Sequential Importance Quadrature Filter
- ACTA ELECTRONICA SINICA Vol. 50, Issue 4, Pages: 823-831(2022)
- 作者机构：
  
  1.中山大学航空航天学院，广东广州 510725
  2.中国科学院空间精密测量技术重点实验室，陕西西安 710119
  3.北京东方计量测试研究所，北京 100029
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210716
  CLC： TN953;
- Received：06 June 2021，
  
  Revised：2022-01-06，
  
  Published：25 April 2022
- 稿件说明：
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张宏伟,张小虎,曹勇.贝叶斯序贯重要性积分滤波器[J].电子学报,2022,50(04):823-831.

ZHANG Hong-wei,ZHANG Xiao-hu,CAO Yong.Bayesian Sequential Importance Quadrature Filter[J].ACTA ELECTRONICA SINICA,2022,50(04):823-831.
张宏伟,张小虎,曹勇.贝叶斯序贯重要性积分滤波器[J].电子学报,2022,50(04):823-831. DOI： 10.12263/DZXB.20210716.

ZHANG Hong-wei,ZHANG Xiao-hu,CAO Yong.Bayesian Sequential Importance Quadrature Filter[J].ACTA ELECTRONICA SINICA,2022,50(04):823-831. DOI： 10.12263/DZXB.20210716.

摘要

为解决非线性滤波中存在模型歧义和预测偏差情况下似然函数、目标重要性密度函数和实际目标分布不匹配的问题，提出了贝叶斯序贯重要性积分滤波器（Bayesian Sequential Importance Quadrature Filter，SIQF）.为了消减贝叶斯推理中似然函数和目标分布之间的偏差，通过空时软约束定义最新观测的有界似然，截断观测噪声概率密度函数以近似可行域的修正先验.为了调制重要性函数和目标分布的匹配程度，并行对修正和原始先验下的状态进行Gauss-Hermite积分，引入最大相关信息熵构建覆盖多模分布的重要性函数，从而提升序贯重要性采样的多样性和预测协方差的容错性.实验结果表明：相比无迹粒子滤波估计一维单变量增长模型，SIQF算法在无需牺牲计算复杂度的情况下平均误差减小了63%；相比多模型Rao-blackwell粒子滤波器跟踪空域机动目标，SIQF算法的均方根误差减小了33%，所需计算量降低了一个数量级.

Abstract

To solve the mismatch problems between the measurement likelihood function

importance density function and the target true distribution for the nonlinear filtering in the presence of the model attribute ambiguity and prediction bias

we derive and present a Bayesian sequential importance quadrature filter(SIQF) algorithm. To reduce the deviation between the likelihood function and the target true distribution in the Bayesian reference

the bounded measurement likelihood of the latest measurement is defined via the soft spatiotemporal constraint

the modified prior of the feasible area is approximated by truncating the probability density function of the measurement noise. To modulate the matching degree between the importance function and the target distribution

the state under the modified and original priors is evaluated via Gauss-Hermite Kalman filter in parallel

the maximum correntropy criterion is introduced to construct the mixture importance function

both the diversity of sequential importance sample and the tolerance of prediction covariance can be thereby improved. The simulation results show that

compared with the unscented particle filter for the estimation of one-dimensional univariate nonstationary growth model

the average estimate error of the SIQF algorithm has decreased 63% without sacrificing computational complexity. Compared with the multi-model Rao-blackwell particle filter for the maneuvering target tracking in the airspace

the root mean square error of the SIQF algorithm has decreased 33%

and the computational load is reduced by an order of magnitude.

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