强杂波背景下基于变分贝叶斯推理的机载雷达目标跟踪算法

李淑慧; 邓志红; 冯肖雪; 潘峰

doi:10.12263/DZXB.20210374

您当前的位置：

首页 >

文章列表页 >

强杂波背景下基于变分贝叶斯推理的机载雷达目标跟踪算法

学术论文 | 更新时间：2025-12-08

- 强杂波背景下基于变分贝叶斯推理的机载雷达目标跟踪算法
- Variational Bayesian Inference‑Based Airborne Radar Target Tracking Algorithm in Strong Clutter
- 电子学报 2022年50卷第5期页码：1089-1097
- 作者机构：
  
  1.北京理工大学自动化学院，北京 100081
  2.昆明北理工产业技术研究院有限公司，云南昆明 650000
- 作者简介：
  
  [ "李淑慧　女，1991年生于河南省商丘市.博士研究生.主要研究方向为目标跟踪、多传感器融合、状态估计. E‑mail: 2579371789@qq.com" ]
  [ "邓志红　女，1974年出生于辽宁省昌图县.现为北京理工大学自动化学院教授，博士生导师.主要从事导航制导与控制领域的研究工作.E‑mail: dzh_deng@bit.edu.cn" ]
  [ "冯肖雪　女，1988年出生于河北省邢台市.现为北京理工大学自动化学院讲师，从事多源信息融合、状态估计方面的研究工作. E‑mail: fengxiaoxue@bit.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61433003;61603040);广东省科技创新战略专项基金(skjtdzxrwqd2018001);云南省科技厅重点研究项目(2018BA070);云南省应用基础研究项目(201701CF00037)
- DOI：10.12263/DZXB.20210374
  中图分类号： TN953
- 收稿：2021-03-21，
  
  修回：2021-06-07，
  
  纸质出版：2022-05-25
- 稿件说明：
移动端阅览
李淑慧,邓志红,冯肖雪等.强杂波背景下基于变分贝叶斯推理的机载雷达目标跟踪算法[J].电子学报,2022,50(05):1089-1097.

LI Shu-hui,DENG Zhi-hong,FENG Xiao-xue,et al.Variational Bayesian Inference‑Based Airborne Radar Target Tracking Algorithm in Strong Clutter[J].ACTA ELECTRONICA SINICA,2022,50(05):1089-1097.
李淑慧,邓志红,冯肖雪等.强杂波背景下基于变分贝叶斯推理的机载雷达目标跟踪算法[J].电子学报,2022,50(05):1089-1097. DOI： 10.12263/DZXB.20210374.

LI Shu-hui,DENG Zhi-hong,FENG Xiao-xue,et al.Variational Bayesian Inference‑Based Airborne Radar Target Tracking Algorithm in Strong Clutter[J].ACTA ELECTRONICA SINICA,2022,50(05):1089-1097. DOI： 10.12263/DZXB.20210374.

摘要

机载雷达遭受的强杂波干扰以及目标的强机动使噪声呈现长拖尾的非高斯特性. 此外，载机的运动导致杂波淹没目标的航迹，使雷达无法检测到目标，出现随机的量测丢失现象. 为此，设计了强杂波背景下含量测丢失的目标跟踪算法. 该算法采用学生t分布来模拟非高斯噪声的长拖尾特性. 通过引入伯努利随机变量，将求和形式的后验概率密度函数转换成乘积形式的概率质量函数，并构建了分层状态空间模型. 在此基础上，设计了用于量测丢失的鲁棒变分贝叶斯平滑器. 以机载雷达跟踪空中目标为例验证了本文算法的有效性.

Abstract

The strong clutter interference suffered by the airborne radar and the strong maneuvering of the target make noise non-Gaussian and heavy-tailed. Besides

the movement of the carrier aircraft induces the target is totally submerged by the clutter

so that the radar cannot detect the target. To this end

a target tracking algorithm for missing measurements in strong clutter is designed. Student t distribution is utilized to model the heavy-tailed property of non-Gaussian noise. The posterior probability density function(PDF) of the summation form is converted into the probability mass function(PMF) of the product form by introducing Bernoulli random variables. And a hierarchical state space model is further devised. Based on this model

a robust variational Bayesian smoother for measurement dropouts(RVBSD) is designed. An example that the airborne radar tracks an airborne target is given to verify the effectiveness of the proposed algorithm.

关键词

Keywords

references

魏立兴 , 孙合敏 , 吴卫华 , 等 . 改进的GM-CBMeMBer机载多普勒雷达多目标跟踪算法 [J]. 空军预警学院学报 , 2017 , 31 ( 3 ): 162 ‑ 166 .

WEI Li-xing , SUN He-min , WU Wei-hua , et al . An improved multi-target tracking algorithm based on GM-CBMeMBer for airborne Doppler radar [J]. Journal of Air Force Early Warning Academy , 2017 , 31 ( 3 ): 162 ‑ 166 . (in Chinese)

SALARI S , CHAN F , CHAN Y T , et al . Compressive sensing-based joint range-doppler and clutter estimation [J]. IEEE Transactions on Aerospace and Electronic Systems , 2019 , 55 ( 6 ): 3207 ‑ 3217 .

KUANG C M , WANG C J , WEN BY , et al . An improved CA-CFAR method for ship target detection in strong clutter using UHF radar [J]. IEEE Signal Processing Letters , 2020 , 27 : 1445 ‑ 1449 .

SANGSTON K J , GINI F , GRECO M S . Coherent radar target detection in heavy-tailed compound Gaussian clutter [J]. IEEE Transactions on Aerospace & Electronic Systems , 2012 , 48 ( 1 ): 64 ‑ 77 .

刘宝泉 , 刘春静 , 代泽洋 . 机载预警雷达KA-STAP杂波抑制方法研究 [J]. 雷达科学与技术 , 2019 , 17 ( 4 ): 396 ‑ 400 .

LIU Bao-quan , LIU Chun-jing , DAI Ze-yang . A KA-STAP based clutter suppression method for airborne early warning radar [J]. Radar Science and Technology , 2019 , 17 ( 4 ): 396 ‑ 400 . (in Chinese)

ARDESHIRI T , OZKAN E , ORGUNER U . Approximate Bayesian smoothing with unknown process and measurement noise covariances [J]. IEEE Signal Processing Letter . 2015 , 22 ( 12 ): 2450 ‑ 2454 .

ZHAO J B , MILI L , et al . Robust unscented Kalman filter for power system dynamic state estimation with unknown noise statistics [J]. IEEE Transactions on Smart Grid , 2019 , 10 ( 2 ): 1215 ‑ 1224 .

ZHAO J B , MILI L . A decentralized H-infinity unscented Kalman filter for dynamic state estimation against uncertainties [J]. IEEE Transactions on Smart Grid , 2019 , 10 ( 5 ): 4870 ‑ 4880 .

XU H , XIE W C , YUAN H D , et al . Fixed-point iteration Gaussian sum filtering estimator with unknown time-varying non-Gaussian measurement noise [J]. Signal Processing , 2018 , 153 : 132 ‑ 142 .

HUANG Y L , ZHANG Y G , Li N , et al . A robust Gaussian approximate fixed-interval smoother for nonlinear systems with heavy-tailed process and measurement noises [J]. IEEE Signal Processing Letters , 2016 , 23 ( 4 ): 468 ‑ 472 .

MA K , XU L , FAN H . Unscented Kalman filtering for target tracking systems with packet dropout compensation [J]. IET Control Theory & Applications , 2019 , 13 ( 12 ): 1901 ‑ 1908 .

JIANG S , FANG H J , et al . Fault estimation for nonlinear networked systems with time-varying delay and random packet dropout [J]. Asian Journal of Control , 2012 , 16 ( 1 ): 126 ‑ 137 .

PUNCHIHEWA Y G , VO B T , VO B N , et al . Multiple object tracking in unknown backgrounds with labeled random finite sets [J]. IEEE Transactions on Signal Processing , 2018 , 66 ( 11 ): 3040 ‑ 3055 .

LI G H , K L J , YI W , et al . Robust Poisson multi-Bernoulli mixture filter with unknown detection probability [J]. IEEE Transactions on Vehicular Technology , 2021 , 70 ( 1 ): 886 ‑ 899 .

BILIK I , TABRIKIAN J . Maneuvering target tracking in the presence of glint using the nonlinear Gaussian mixture Kalman filter [J]. IEEE Transactions on Aerospace & Electronic Systems , 2010 , 46 ( 1 ): 246 ‑ 262 .

DONG P , JING Z L , LEUNG H , et al . The labeled multi-Bernoulli filter for multitarget tracking with glint noise [J]. IEEE Transactions on Aerospace and Electronic Systems , 2019 , 55 ( 5 ): 2253 ‑ 2268 .

BORDEN B , MUMFORD M . A statistical glint/radar cross section target model [J]. IEEE Transactions on Aerospace and Electronic Systems , 1983 , AES- 19 ( 1 ): 781 ‑ 785 .

BISHOP C M . Pattern Recognition and Machine Learning (Information Science and Statistics) [M]. New York : Springer-Verlag , 2006 .

SIMO S . Bayesian Filtering and Smoothing [M]. New York : Cambridge University Press , 2013 .

PETSIOS M N , ALIVIZATOS E G , UZUNOGLU N K . Manoeuvring target tracking using multiple bistatic range and range-rate measurements [J]. Signal Processing , 2007 , 87 ( 4 ): 665 ‑ 686 .

ARASARATNAM I , HAYKIN S . Cubature Kalman smoothers [J]. Automatic , 2011 , 47 ( 10 ): 2245 ‑ 2250 .

黄玉龙 . 高精度状态估计方法研究及其在目标跟踪和协同定位中的应用 [D]. 哈尔滨 : 哈尔滨工程大学 , 2019 .

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

L波段HH极化机载雷达杂波特性分析

一种基于回波数据的机载雷达通道均衡的方法

基于俯仰维信息的机载雷达非均匀杂波抑制方法