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

doi:10.12263/DZXB.20210374

电子学报 ›› 2022, Vol. 50 ›› Issue (5): 1089-1097.DOI: 10.12263/DZXB.20210374

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¹, 邓志红¹, 冯肖雪¹, 潘峰¹^,²

^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

收稿日期:2021-03-21 修回日期:2021-06-07 出版日期:2022-05-25 发布日期:2022-06-18
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
基金资助:
国家自然科学基金(61433003);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)

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

LI Shu-hui¹, DENG Zhi-hong¹, FENG Xiao-xue¹, PAN Feng¹^,²

^1.School of Automation，Beijing Institute of Technology，Beijing 100081，China
^2.Kunming?BIT Industry Technology Research Institute INC，Kunming，Yunnan 650000，China

Received:2021-03-21 Revised:2021-06-07 Online:2022-05-25 Published:2022-06-18

摘要/Abstract

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.

关键词: 机载雷达, 杂波, 量测丢失, 概率图模型, 变分贝叶斯推理, 多变量学生t分布

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.

Key words: airborne radar, clutter, missing measurement, probabilistic graph model, variational Bayesian inference, multivariable Student t distribution

中图分类号:

TN953

李淑慧, 邓志红, 冯肖雪, 潘峰. 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(5): 1089-1097.

LI Shu-hui, DENG Zhi-hong, FENG Xiao-xue, PAN Feng. Variational Bayesian Inference‑Based Airborne Radar Target Tracking Algorithm in Strong Clutter[J]. Acta Electronica Sinica, 2022, 50(5): 1089-1097.

图/表 8

参考文献 22

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方法	位置 ARMSEs	速度 ARMSEs	加速度 ARMSEs	时间（s）
RVBSD	3.4328	2.3616	1.2866	0.4516
CKS	6761.45	1239.73	453.93	0.0188
CKSD	4741.39	135.017	5.7307	0.0213
VBCKS_QRwv	6.1291	3.0761	1.5639	0.3992
VBCKS_QR	7.0944	3.1896	1.5742	0.3950
VBCKS_wv	85.6035	19.4387	6.8943	0.3960

方法	位置 ARMSEs	速度 ARMSEs	加速度 ARMSEs	时间（s）
RVBSD	3.4328	2.3616	1.2866	0.4516
CKS	6761.45	1239.73	453.93	0.0188
CKSD	4741.39	135.017	5.7307	0.0213
VBCKS_QRwv	6.1291	3.0761	1.5639	0.3992
VBCKS_QR	7.0944	3.1896	1.5742	0.3950
VBCKS_wv	85.6035	19.4387	6.8943	0.3960

方法	位置 AAVBs	速度 AAVBs	加速度 AAVBs
RVBSD	5.1441	3.5477	1.924
CKS	8667.60	1601.73	578.04
CKSD	6756.31	182.64	8.4287
VBCKS_QRwv	8.8643	4.5505	2.317
VBCKS_QR	10.408	4.8979	2.3106
VBCKS_wv	106.56	24.479	8.7954

方法	位置 AAVBs	速度 AAVBs	加速度 AAVBs
RVBSD	5.1441	3.5477	1.924
CKS	8667.60	1601.73	578.04
CKSD	6756.31	182.64	8.4287
VBCKS_QRwv	8.8643	4.5505	2.317
VBCKS_QR	10.408	4.8979	2.3106
VBCKS_wv	106.56	24.479	8.7954

情形	位置ARMSEs	速度ARMSEs	加速度ARMSEs
1	3.4328	2.3616	1.2866
2	3.0782	1.8206	1.101
3	4.2133	2.3614	1.1414
4	15794.3	519.27	36.708
5	14.1213	4.3291	1.6998
6	2968.90	118.591	5.598

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

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参考文献 22

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情形	位置AAVBs	速度AAVBs	加速度AAVBs
1	5.1441	3.5477	1.924
2	4.6013	2.7761	1.6323
3	6.1762	3.407	1.6653
4	20875.2	685.78	45.707
5	19.8595	6.4589	2.5116
6	4076.759	175.585	8.1953

情形	位置AAVBs	速度AAVBs	加速度AAVBs
1	5.1441	3.5477	1.924
2	4.6013	2.7761	1.6323
3	6.1762	3.407	1.6653
4	20875.2	685.78	45.707
5	19.8595	6.4589	2.5116
6	4076.759	175.585	8.1953