基于PHD滤波的雷达起伏目标检测前跟踪算法研究

doi:10.12263/DZXB.20210079

电子学报 ›› 2022, Vol. 50 ›› Issue (3): 691-702.DOI: 10.12263/DZXB.20210079

基于PHD滤波的雷达起伏目标检测前跟踪算法研究

吴孙勇¹^,³, 李东升¹, 薛秋条¹, 孙希延², 蔡如华¹

^1.桂林电子科技大学数学与计算科学学院, 广西桂林 541004
^2.广西信息科学实验中心, 广西桂林 541004
^3.广西密码学与信息安全重点实验室, 广西桂林 541004

收稿日期:2021-01-07 修回日期:2021-07-05 出版日期:2022-03-25
- 作者简介:
- 吴孙勇男，1981年生，广西桂林人，博士，教授，主要研究方向为多目标检测与跟踪，雷达信号处理，随机有限集等.E-mail:wusunyong121991@163.com.
  李东升男，1994年生，甘肃天水人，硕士研究生，主要研究方向为微弱目标检测与跟踪，雷达信号处理等.E-mail:1657268543@qq.com.
  薛秋条（通讯作者）女,1978年生，硕士，副教授，主要研究方向为微弱目标检测与跟踪，阵列信号处理，随机有限集等.
- 基金资助:
- 国家自然科学基金 (61861008); 广西自然科学基金 (2016GXNSFAA380073); 桂林电子科技大学数学与计算科学学院论文培优项目 (2020YJSPYB01); 广西密码学与信息安全重点实验室开放基金; 广西高校数据分析与计算重点实验室开放基金; 大学生创新训练项目 (201910595164)

Research on PHD Filter Based Track-Before-Detect Algorithm of Radar Fluctuating Targets

WU Sun-yong¹^,³, LI Dong-sheng¹, XUE Qiu-tiao¹, SUN Xi-yan², CAI Ru-hua¹

^1.Mathematics and Computer Science College of Guilin University of Electronic Technology, Guilin, Guangxi 541004 China
^2.Guangxi Information Science Experiment Center, Guilin, Guangxi 541004 China
^3.Guangxi Key Laboratory of Cryptography and Information Security, Guilin, Guangxi 541004 China

Received:2021-01-07 Revised:2021-07-05 Online:2022-03-25 Published:2022-03-25
- Supported by:
- National Natural Science Foundation of China (61861008); Natural Science Foundation of Guangxi Zhuang Autonomous Region, China (2016GXNSFAA380073); Dissertation Training Program of School of Mathematics and Computing Science, Guilin University of Electronic Technology (2020YJSPYB01); Open Foundation of Guangxi Key Laboratory of Cryptography and Information Security; Open Fund for Key Laboratory of Data Analysis and Computing of Univerities in Guangxi Province; University Students' Innovative Training Program (201910595164)

摘要/Abstract

摘要：

针对雷达微弱起伏目标的检测和跟踪问题，研究了Swerling 0,1,3 三类起伏目标模型，提出了概率假设密度滤波下幅度起伏的雷达微弱目标检测前跟踪算法.该算法建立了概率假设密度检测前跟踪算法下复似然比和幅度似然比两种跟踪模型，其中复似然比方法弥补了幅度似然比在计算过程中只考虑量测的幅度信息，而忽略相位信息的缺陷，从而更好地利用了目标原始信息.同时，为解决新生目标状态先验分布信息未知条件下的目标新生问题，提出一种场景划分下基于量测似然比的自适应目标新生算法.仿真实验结果表明，在目标幅度起伏的情况下，复似然比和幅度似然比相比，前者在目标位置和个数的估计性能上优于后者，且计算效率更高.在低信噪比下，复似然比仍然可以有效地检测并跟踪未知数量的微弱目标.

关键词: 检测前跟踪, 微弱目标, 概率假设密度滤波器, 幅度起伏, 自适应新生

Abstract:

Aiming at the the detection and tracking of weak radar targets, the Swerling 0,1,3 three types of fluctuating target models are studied, and track-before-detect algorithm of weak radar targets with amplitude fluctuations based on probability hypothesis density filtering is proposed. We establish two tracking models of complex likelihood ratio and amplitude likelihood ratio under the probability hypothesis density track-before-detect algorithm. As results of making better use of the original target information, the complex likelihood ratio approach makes up for the shortcomings of the amplitude likelihood ratio. In order to solve the problem of newborn targets with unknown prior distribution information, we propose the adaptive target birth algorithm based on measurement likelihood ratio. The simulation results show that, in the case of target amplitude fluctuation, the complex likelihood ratio method is superior to the amplitude likelihood ratio in the estimation performance of target position and number, and the calculation efficiency is higher. At low signal-to-noise ratio, complex likelihood can still detect and track unknown number of weak targets effectively.

Key words: track-before-detect, weak targets, probability hypothesis density filter, amplitude fluctuations, adaptive birth

中图分类号:

TN957.51

吴孙勇, 李东升, 薛秋条, 等. 基于PHD滤波的雷达起伏目标检测前跟踪算法研究[J]. 电子学报, 2022, 50(3): 691-702.

Sun-yong WU, Dong-sheng LI, Qiu-tiao XUE, et al. Research on PHD Filter Based Track-Before-Detect Algorithm of Radar Fluctuating Targets[J]. Acta Electronica Sinica, 2022, 50(3): 691-702.

图/表 10

图1 不同信噪比下目标回波

表3 模拟数值

参数	数值	参数	数值	参数	数值
$r m i n$	$100 k m$	$N r$	$300$	$λ$	$3 c m$
$r m a x$	$120 k m$	$N θ$	$100$	$c$	$3 × 108 m / s$
$θ m i n$	$- 75 ∘$	$B$	$150 K H z$	$Δ r$	$500 m$
$θ m a x$	$75 ∘$	$T e$	$6.67 × 10 - 5 s$	$Δ θ$	$1 . 45 ∘$
$σ 2$	$0.5$	$N a$	$50$	$S N R$	$10 l o g 10 E (ρ k 2) / (2 σ 2)$

表3 模拟数值

参数	数值	参数	数值	参数	数值
$r m i n$	$100 k m$	$N r$	$300$	$λ$	$3 c m$
$r m a x$	$120 k m$	$N θ$	$100$	$c$	$3 × 108 m / s$
$θ m i n$	$- 75 ∘$	$B$	$150 K H z$	$Δ r$	$500 m$
$θ m a x$	$75 ∘$	$T e$	$6.67 × 10 - 5 s$	$Δ θ$	$1 . 45 ∘$
$σ 2$	$0.5$	$N a$	$50$	$S N R$	$10 l o g 10 E (ρ k 2) / (2 σ 2)$

表4 目标初始状态

目标初始状态（m）	出现时刻（s）	消失时刻（s）
$x 1 = [94815,435,9088,457] T$	1	65
$x 2 = [90472,331,20430,460] T$	5	95
$x 3 = [81837,223, - 58736,763] T$	12	100
$x 4 = [123862,367,85049,644] T$	20	70
$x 5 = [138136,519, - 48036,498] T$	42	100

表4 目标初始状态

目标初始状态（m）	出现时刻（s）	消失时刻（s）
$x 1 = [94815,435,9088,457] T$	1	65
$x 2 = [90472,331,20430,460] T$	5	95
$x 3 = [81837,223, - 58736,763] T$	12	100
$x 4 = [123862,367,85049,644] T$	20	70
$x 5 = [138136,519, - 48036,498] T$	42	100

图2 目标运动的真实轨迹

图3 Swerling 0下复似然比和幅度似然比算法对比图

图4 Swerling 1下复似然比和幅度似然比算法对比图

图5 Swerling 3下复似然比和幅度似然比算法对比图

图6 Swerling 0类型不同信噪比下复似然比和幅度似然比算法对比图

图7 Swerling 1类型不同信噪比下复似然比和幅度似然比算法对比图

图8 Swerling 3类型不同信噪比下复似然比和幅度似然比算法对比图

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基于PHD滤波的雷达起伏目标检测前跟踪算法研究

Research on PHD Filter Based Track-Before-Detect Algorithm of Radar Fluctuating Targets

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