贝叶斯序贯重要性积分滤波器

张宏伟; 张小虎; 曹勇

doi:10.12263/DZXB.20210716

您当前的位置：

首页 >

文章列表页 >

贝叶斯序贯重要性积分滤波器

智能时空信息服务技术 | 更新时间：2025-12-08

- 贝叶斯序贯重要性积分滤波器
- Bayesian Sequential Importance Quadrature Filter
- 电子学报 2022年50卷第4期页码：823-831
- 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：
  
  [ "张宏伟女，1982年出生，河南南阳人.博士，中山大学航天航空学院副研究员.主要研究方向为智能信息处理、信息融合、目标跟踪. E-mail: zhanghw69@sysu.edu.cn" ]
  [ "张小虎男，1973年出生，陕西凤翔人.博士，中山大学航天航空学院教授，图像感知与信息处理学科学术带头人.主要研究方向为精密测量、图像信息处理." ]
- 基金信息：
  
  广东省基础与应用基础研究基金(2019A1515111099);中山大学青年培育项目(20lgpy72);中国科学院空间精密测量重点实验室开放基金(SPMT2021002)
- DOI：10.12263/DZXB.20210716
  中图分类号： TN953;
- 收稿：2021-06-06，
  
  修回：2022-01-06，
  
  纸质出版：2022-04-25
- 稿件说明：
移动端阅览
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.

关键词

Keywords

references

GELMAN A , CARLIN J B , STERN H S , et al . Bayesian Data Analysis [M]. New York : Chapman and Hall/CRC , 1995 .

DE-GEETER J , VAN-BRUSSEL H , DE-SCHUTTER J , et al . A smoothly constrained Kalman filter [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 1997 , 19 ( 10 ): 1171 - 1177 .

JULIER S J , UHLMANN J K . Unscented filtering and nonlinear estimation [J]. Proceedings of the IEEE , 2004 , 92 ( 3 ): 401 - 422 .

ARASARATNAM I , HAYKIN S , ELLIOTT R J . Discrete-time nonlinear filtering algorithms using Gauss-hermite quadrature [J]. Proceedings of the IEEE , 2007 , 95 ( 5 ): 953 - 977 .

GARCIA-FERNANDEZ Á F , MORELANDE M R , GRAJAL J . Truncated unscented Kalman filtering [J]. IEEE Transactions on Signal Processing , 2012 , 60 ( 7 ): 3372 - 3386 .

ANDRIEU C , DE-FREITAS N , DOUCET A , et al . An introduction to MCMC for machine learning [J]. Machine Learning , 2003 , 50 ( 1/2 ): 5 - 43 .

BUGALLO M F , ELVIRA V , MARTINO L , et al . Adaptive importance sampling: The past, the present, and the future [J]. IEEE Signal Processing Magazine , 2017 , 34 ( 4 ): 60 - 79 .

DOUCET A , GODSILL S , ANDRIEU C . On sequential Monte Carlo sampling methods for Bayesian filtering [J]. Statistics and Computing , 2000 , 10 ( 3 ): 197 - 208 .

XIE W X , BEDROSIAN S D . An information measure for fuzzy sets [J]. IEEE Transactions on Systems, Man, and Cybernetics , 1984 , SMC- 14 ( 1 ): 151 - 156 .

SIMON D . Kalman filtering with state constraints: A survey of linear and nonlinear algorithms [J]. IET Control Theory & Applications , 2010 , 4 ( 8 ): 1303 - 1318 .

BAR-SHALOM Y , LI X R , KIRUBARAJAN T . Estimation with Applications to Tracking and Navigation [M]. New York : John Wiley & Sons, Inc , 2001 .

TRONARP F , GARCÍA-FERNÁNDEZ Á F , SÄRKKÄ S . Iterative filtering and smoothing in nonlinear and non-Gaussian systems using conditional moments [J]. IEEE Signal Processing Letters , 2018 , 25 ( 3 ): 408 - 412 .

VAN-DER-MERWE R , DOUCET A , DE-FREITAS N , et al . The unscented particle filter [J]. Advances in Neural Information Processing Systems , 2000 , 13 : 584 - 590 .

ELVIRA V , MARTINO L , CLOSAS P . Importance Gaussian quadrature [J]. IEEE Transactions on Signal Processing , 2021 , 69 : 474 - 488 .

GARCIA-FERNANDEZ Á F , MORELANDE M R , GRAJAL J . Truncated unscented Kalman filtering [J]. IEEE Transactions on Signal Processing , 2012 , 60 ( 7 ): 3372 - 3386 .

KIRUBARAJAN T , BAR-SHALOM Y . Kalman filter versus IMM estimator: When do we need the latter? [J]. IEEE Transactions on Aerospace and Electronic Systems , 2003 , 39 ( 4 ): 1452 - 1457 .

HOSTETTLER R , SÄRKKÄ S . Rao-blackwellized Gaussian smoothing [J]. IEEE Transactions on Automatic Control , 2019 , 64 ( 1 ): 305 - 312 .

王琪 , 廖志忠 , 燕飞 . 基于 Rao-Blackwellised 粒子滤波的相控阵导引头指向误差斜率在线估计 [J]. 电子与信息学报 , 2021 , 43 : 1 - 8 .

WANG Q , LIAO Z Z , YAN F . Online estimation for phased array seeker pointing error slope using rao-blackwellised particle filters [J]. Journal of Electronics and Information Technology , 2021 , 43 : 1 - 8 . (in Chinese)

ZHANG H W , XIE W X . Constrained unscented Kalman filtering for bearings-only maneuvering target tracking [J]. Chinese Journal of Electronics , 2020 , 29 ( 3 ): 501 - 507 .

LIU C J , LI B B , CHEN W H . Particle filtering with soft state constraints for target tracking [J]. IEEE Transactions on Aerospace and Electronic Systems , 2019 , 55 ( 6 ): 3492 - 3504 .

蒋林 , 向超 , 朱建阳 , 等 . 加载语义似然估计的粒子滤波重定位 [J]. 电子学报 , 2021 , 49 ( 2 ): 306 - 314 .

JIANG L , XIANG C , ZHU J Y , et al . Particle filter relocation with semantic likelihood estimation [J]. Acta Electronica Sinica , 2021 , 49 ( 2 ): 306 - 314 . (in Chinese)

仇祝令 , 查宇飞 , 吴敏 , 等 . 基于注意力学习的正则化相关滤波跟踪算法 [J]. 电子学报 , 2020 , 48 ( 9 ): 1762 - 1768 .

QIU Z L , ZHA Y F , WU M , et al . Learning attentional regularized correlation filter for visual tracking [J]. Acta Electronica Sinica , 2020 , 48 ( 9 ): 1762 - 1768 . (in Chinese)

AFTAB W , MIHAYLOVA L . A learning Gaussian process approach for maneuvering target tracking and smoothing [J]. IEEE Transactions on Aerospace and Electronic Systems , 2020 , 57 ( 1 ): 278 - 292 .

RONG LI X . Compatibility and modeling of constrained dynamic systems [C]// 2016 19th International Conference on Information Fusion(FUSION) . New York, USA : IEEE , 2016 : 240 - 247 .

中山大学 . 一种基于贝叶斯序贯重要性积分的卡尔曼滤波方法： CN202110720593.9 ［P］. 2021-9-28 .

PENG J T , GUO L , HU Y , et al . Maximum correntropy criterion based regression for multivariate calibration [J]. Chemometrics and Intelligent Laboratory Systems , 2017 , 161 : 27 - 33 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

一种抑制符号速率估计背景色噪声的非线性滤波算法

基于动力学模型约束的空间目标精确跟踪算法研究