Extended Target Multi-Feature Estimation Adaptive Progressive Filter

WANG Xu-xin; CHEN Hui; LIAN Feng; ZHANG Guang-hua

doi:10.12263/DZXB.20230873

您当前的位置：

首页 >

文章列表页 >

Extended Target Multi-Feature Estimation Adaptive Progressive Filter

PAPERS | 更新时间：2025-12-08

- Extended Target Multi-Feature Estimation Adaptive Progressive Filter
- ACTA ELECTRONICA SINICA Vol. 52, Issue 9, Pages: 3135-3147(2024)
- 作者机构：
  
  1.兰州理工大学电气工程与信息工程学院，甘肃兰州 730050
  2.西安交通大学自动化科学与工程学院，陕西西安 710049
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62163023;62173266;62103318;61873116);Industrial Support Project of Education Department of Gansu Province(2021CYZC-02);Special Funds Project for Civil-Military Integration Development in Gansu Province in 2023
- DOI：10.12263/DZXB.20230873
  CLC： TP274
- Received：19 September 2023，
  
  Revised：2024-03-01，
  
  Published：25 September 2024
- 稿件说明：
移动端阅览
王旭昕, 陈辉, 连峰, 等. 扩展目标多特征估计自适应渐进滤波器[J]. 电子学报, 2024, 52(09): 3135-3147.

WANG Xu-xin, CHEN Hui, LIAN Feng, et al. Extended Target Multi-Feature Estimation Adaptive Progressive Filter[J]. Acta Electronica Sinica, 2024, 52(09): 3135-3147.
王旭昕, 陈辉, 连峰, 等. 扩展目标多特征估计自适应渐进滤波器[J]. 电子学报, 2024, 52(09): 3135-3147. DOI：10.12263/DZXB.20230873

WANG Xu-xin, CHEN Hui, LIAN Feng, et al. Extended Target Multi-Feature Estimation Adaptive Progressive Filter[J]. Acta Electronica Sinica, 2024, 52(09): 3135-3147. DOI：10.12263/DZXB.20230873

摘要

针对具有不规则形状的扩展目标跟踪（Extended Target Tracking，ETT）问题，本文提出了一种基于随机超曲面模型的自适应渐进贝叶斯滤波器（Random Hypersurface Model-Adaptive Progressive Bayesian Filter，RHM-APBF）.首先，对扩展目标连续状态先验概率密度的局部累积分布进行随机采样，再最小化连续概率密度和狄拉克混合概率密度的局部累积分布之间的修正克莱默冯米塞斯距离得到粒子的最优位置，以自适应地变步长进而渐进更新将粒子迁移到扩展目标后验的密集区域求得更加准确的后验概率密度近似；其次，利用随机超曲面描述任意星凸形扩展目标的量测源分布，提出了星凸形不规则形状扩展目标跟踪自适应渐进滤波器，有效实现了不规则形状扩展目标多特征概率密度信息的递归.最后通过不同噪声水平以及复杂随机环境的扩展目标（Extended Target，ET）和群目标（Group Target，GT）的跟踪仿真实验验证本文方法的有效性.

Abstract

To address the problem of extended target tracking (ETT) with irregular shape

this paper proposes a random hypersurface model-adaptive progressive bayesian filter (RHM-APBF). First

the local cumulative distribution of the continuous state prior probability density of extended target is randomly sampled

and the optimal position of the sampling point is obtained by minimizing the modified Cramer-Von Mises distance between the local cumulative distribution of the continuous probability density and the Dirac mixture probability density. Then

the sampled particles are migrated to the posterior dense area to obtain a more accurate posterior probability density approximation by progressive update with adaptive variable step size. Furthermore

the random hypersurface model is used to represent the measurement source distribution of arbitrary star-convex extended targets

and an adaptive progressive filter for tracking star-convex irregular shape extended target is proposed

which effectively recurses the multi-feature probability density of irregular shape extended targets. Finally

the effectiveness of the proposed method is verified by the tracking simulation experiments of the extended target (ET) and group target (GT) at different noise level and complex random environment.

关键词

Keywords

references

WU J D , HUANG Z Y , LV C . Uncertainty-aware model-based reinforcement learning: Methodology and application in autonomous driving [J ] . IEEE Transactions on Intelligent Vehicles , 2023 , 8 ( 1 ): 194 - 203 .

ENGELS F , HEIDENREICH P , WINTERMANTEL M , et al . Automotive radar signal processing: Research directions and practical challenges [J ] . IEEE Journal of Selected Topics in Signal Processing , 2021 , 15 ( 4 ): 865 - 878 .

HAKOBYAN G , YANG B . High-performance automotive radar: A review of signal processing algorithms and modulation schemes [J ] . IEEE Signal Processing Magazine , 2019 , 36 ( 5 ): 32 - 44 .

LI Y , IBANEZ-GUZMAN J . Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems [J ] . IEEE Signal Processing Magazine , 2020 , 37 ( 4 ): 50 - 61 .

CAO X M , LAN J , LI X R . Extension-deformation approach to extended object tracking [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2021 , 57 ( 2 ): 866 - 881 .

胡振涛 , 杨诗博 , 胡玉梅 , 等 . 基于变分贝叶斯的分布式融合目标跟踪 [J ] . 电子学报 , 2022 , 50 ( 5 ): 1058 - 1065 .

HU Z T , YANG S B , HU Y M , et al . Distributed fusion target tracking based on variational Bayes [J ] . Acta Electronica Sinica , 2022 , 50 ( 5 ): 1058 - 1065 . (in Chinese)

LI Z F , LIANG Y , XU L F , et al . Distributed extended object tracking information filter over sensor networks [J ] . International Journal of Robust and Nonlinear Control , 2023 , 33 ( 2 ): 1122 - 1149 .

FELDMANN M , FRANKEN D , KOCH W . Tracking of extended objects and group targets using random matrices [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 4 ): 1409 - 1420 .

ANGELOVA D , MIHAYLOVA L , PETROV N , et al . A convolution particle filtering approach for tracking elliptical extended objects [C ] // Proceedings of the 16th International Conference on Information Fusion . Piscataway : IEEE , 2013 : 1542 - 1549 .

YANG S S , BAUM M . Tracking the orientation and axes lengths of an elliptical extended object [J ] . IEEE Transactions on Signal Processing , 2019 , 67 ( 18 ): 4720 - 4729 .

AFTAB W , DE FREITAS A , ARVANEH M , et al . A Gaussian process approach for extended object tracking with random shapes and for dealing with intractable likelihoods [C ] // 2017 22nd International Conference on Digital Signal Processing (DSP) . Piscataway : IEEE , 2017 : 1 - 5 .

WAHLSTROM N , OZKAN E . Extended target tracking using Gaussian processes [J ] . IEEE Transactions on Signal Processing , 2015 , 63 ( 16 ): 4165 - 4178 .

KUMRU M , KOKSAL H , OZKAN E . Variational measurement update for extended object tracking using Gaussian processes [J ] . IEEE Signal Processing Letters , 2021 , 28 : 538 - 542 .

KUMRU M , OZKAN E . Three-dimensional extended object tracking and shape learning using Gaussian processes [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2021 , 57 ( 5 ): 2795 - 2814 .

YAO G , WANG P , BERNTORP K , et al . Extended object tracking with spatial model adaptation using automotive radar [C ] // 2021 IEEE 24th International Conference on Information Fusion (FUSION) . Piscataway : IEEE , 2021 : 1 - 8 .

DAHLEN K M , LINDBERG C , YONEDA M , et al . An improved b-spline extended object tracking model using the iterative closest point method [C ] // 2022 25th International Conference on Information Fusion (FUSION) . Piscataway : IEEE , 2022 : 1 - 8 .

KAULBERSCH H , HONER J , BAUM M . A Cartesian B-spline vehicle model for extended object tracking [C ] // 2018 21st International Conference on Information Fusion (FUSION) . Piscataway : IEEE , 2018 : 1 - 5 .

NAUJOKS B , BURGER P , WUENSCHE H J . Fast 3D extended target tracking using nurbs surfaces [C ] // 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . Piscataway : IEEE , 2019 : 1104 - 1109 .

BAUM M , HANEBECK U D . Extended object tracking with random hypersurface models [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2014 , 50 ( 1 ): 149 - 159 .

ÖZKAN E , WAHLSTROM N , GODSILL S . Rao-Blackwellised particle filter for star-convex extended target tracking models [C ] // 2016 19th International Conference on Information Fusion . Piscataway : IEEE , 2016 : 1193 - 1199 .

SUN L F , LAN J , LI X . Extended target tracking using star-convex model with nonlinear inequality constraints [C ] // Proceedings of the 31st Chinese Control Conference , Piscataway : IEEE , 2012 : 3869 - 3874 .

陈辉 , 杜金瑞 , 韩崇昭 . 基于星凸形随机超曲面模型多扩展目标多伯努利滤波器 [J ] . 自动化学报 , 2020 , 46 ( 5 ): 909 - 922 .

CHEN H , DU J R , HAN C Z . A multiple extended target multi-bernouli filter based on star-convex random hypersurface model [J ] . Acta Automatica Sinica , 2020 , 46 ( 5 ): 909 - 922 . (in Chinese)

BAUM M , NOACK B , HANEBECK U D . Mixture random hypersurface models for tracking multiple extended objects [C ] // IEEE Conference on Decision and Control and European Control Conference . Piscataway : IEEE , 2011 : 3166 - 3171 .

KALMAN R E . A new approach to linear filtering and prediction problems [J ] . Journal of Basic Engineering , 1960 , 82 ( 1 ): 35 - 45 .

DAN S . Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches [M ] . Hoboken : Wiley-Interscience , 2006 .

BAR-SHALOM Y , LI X R , KIRUBARAJAN T . Estimation with Applications to Tracking and Navigation [M ] . New York : John Wiley & Sons Inc , 2004 .

王国庆 , 杨春雨 , 马磊 . 基于多变量Laplace分布的非线性系统分布式鲁棒状态估计 [J ] . 电子学报 , 2022 , 50 ( 11 ): 2610 - 2618 .

WANG G Q , YANG C Y , MA L . Distributed robust state estimation for nonlinear systems based on multivariate laplace distribution [J ] . Acta Electronica Sinica , 2022 , 50 ( 11 ): 2610 - 2618 . (in Chinese)

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 . Cubature Kalman filters [J ] . IEEE Transactions on Automatic Control , 2009 , 54 ( 6 ): 1254 - 1269 .

REIF K , GUNTHER S , YAZ E , et al . Stochastic stability of the discrete-time extended Kalman filter [J ] . IEEE Transactions on Automatic Control , 1999 , 44 ( 4 ): 714 - 728 .

GARCIA-FERNANDEZ A F , RALPH J , HORRIDGE P , et al . A Gaussian filtering method for multitarget tracking with nonlinear/non-gaussian measurements [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2021 , 57 ( 5 ): 3539 - 3548 .

GARCIA-FERNANDEZ A F , SVENSSON L , SARKKA S . Iterated posterior linearization smoother [J ] . IEEE Transactions on Automatic Control , 2017 , 62 ( 4 ): 2056 - 2063 .

RAITOHARJU M , SVENSSON L , GARCIA-FERNANDEZ A F , et al . Damped posterior linearization filter [J ] . IEEE Signal Processing Letters , 2018 , 25 ( 4 ): 536 - 540 .

SKOGLUND M A , GUSTAFSSON F , HENDEBY G . On iterative unscented Kalman filter using optimization [C ] // 2019 22th International Conference on Information Fusion (FUSION) . Piscataway : IEEE , 2019 : 1 - 8 .

KULLBERG A , SKOG I , HENDEBY G . Iterated filters for nonlinear transition models [EB/OL ] . ( 2023-02-27 )[ 2023-08-10 ] . http://arxiv.org/abs/2302.13871 http://arxiv.org/abs/2302.13871 .

RISTIC B , ARULAMPALAM S , GORDON N . Beyond the Kalman Filter: Particle Filters for Tracking Applications [M ] . London : Artech House , 2004 .

KOTECHA J H , DJURIC P M . Gaussian sum particle filtering [J ] . IEEE Transactions on Signal Processing , 2003 , 51 ( 10 ): 2602 - 2612 .

PETROV N , MIHAYLOVA L , GNING A , et al . A novel sequential Monte Carlo approach for extended object tracking based on border parameterisation [C ] // 14th International Conference on Information Fusion . Piscataway : IEEE , 2011 : 1 - 8 .

HANEBECK U D , KLUMPP V . Localized Cumulative Distributions and a multivariate generalization of the Cramér-von Mises distance [C ] // 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems . Piscataway : IEEE , 2008 : 33 - 39 .

HÄRDLE W K , SIMAR L . Applied Multivariate Statistical Analysis [M ] . Berlin, Heidelberg : Springer Berlin Heidelberg , 2012 .

STEINBRING J , BAUM M , ZEA A , et al . A closed-form likelihood for particle filters to track extended objects with star-convex RHMs [C ] // 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) . Piscataway : IEEE , 2015 : 25 - 30 .

FRISCH D , HANEBECK U D . Progressive bayesian filtering with coupled gaussian and dirac mixtures [C ] // 2020 IEEE 23rd International Conference on Information Fusion (FUSION) . Piscataway : IEEE , 2020 : 1 - 8 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰