基于PHD滤波的雷达起伏目标检测前跟踪算法研究

吴孙勇; 李东升; 薛秋条; 孙希延; 蔡如华

doi:10.12263/DZXB.20210079

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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

学术论文 | 更新时间：2025-12-11

- 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
- 电子学报 2022年50卷第3期页码：691-702
- 作者机构：
  
  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
- 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." ]
- 基金信息：
  
  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)
- DOI：10.12263/DZXB.20210079
  中图分类号： TN957.51
- 收稿：2021-01-07，
  
  修回：2021-07-05，
  
  纸质出版：2022-03-25
- 稿件说明：
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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(03):691-702.

WU Sun-yong,LI Dong-sheng,XUE Qiu-tiao,et al.Research on PHD Filter Based Track-Before-Detect Algorithm of Radar Fluctuating Targets[J].ACTA ELECTRONICA SINICA,2022,50(03):691-702.
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(03):691-702. DOI： 10.12263/DZXB.20210079.

WU Sun-yong,LI Dong-sheng,XUE Qiu-tiao,et al.Research on PHD Filter Based Track-Before-Detect Algorithm of Radar Fluctuating Targets[J].ACTA ELECTRONICA SINICA,2022,50(03):691-702. DOI： 10.12263/DZXB.20210079.

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

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.

Abstract

Aiming at the the detection and tracking of weak radar targets

the Swerling 0

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.

关键词

Keywords

references

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