Variational Bayesian Inference‑Based Airborne Radar Target Tracking Algorithm in Strong Clutter

LI Shu-hui; DENG Zhi-hong; FENG Xiao-xue; PAN Feng

doi:10.12263/DZXB.20210374

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Variational Bayesian Inference‑Based Airborne Radar Target Tracking Algorithm in Strong Clutter

PAPERS | 更新时间：2025-12-08

- Variational Bayesian Inference‑Based Airborne Radar Target Tracking Algorithm in Strong Clutter
- ACTA ELECTRONICA SINICA Vol. 50, Issue 5, Pages: 1089-1097(2022)
- 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：
- 基金信息：
- DOI：10.12263/DZXB.20210374
  CLC： TN953
- Received：21 March 2021，
  
  Revised：2021-06-07，
  
  Published：25 May 2022
- 稿件说明：
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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.

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references

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