认知雷达波形和检测门限自适应跟踪算法

王树亮 1; 毕大平 1; 张奎 1; 金培进 2; 陈小坤 3

doi:10.7652/xjtuxb201904010

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认知雷达波形和检测门限自适应跟踪算法

更新时间：2025-07-09

- 认知雷达波形和检测门限自适应跟踪算法
- An Adaptive Tracking Algorithm Based on Waveform and Detection Threshold of Cognitive Radars
- 西安交通大学学报 2019年53卷第4期页码：65-71
- 作者机构：
  
  国防科技大学电子对抗学院,合肥,230037
- 作者简介：
- 基金信息：
  
  : 2018-09-01。作者简介: 王树亮(1984—),男,博士生;毕大平(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61671453);安徽省自然科学基金资助项目(1608085MF123)
- DOI：10.7652/xjtuxb201904010
  中图分类号： TN953
- 网络首发：2019-04-10，
  
  纸质出版：2019
- 稿件说明：
移动端阅览
王树亮 1, 毕大平 1, 张奎 1, 等. 认知雷达波形和检测门限自适应跟踪算法[J]. 西安交通大学学报, 2019,53(4):65-71.

An Adaptive Tracking Algorithm Based on Waveform and Detection Threshold of Cognitive Radars[J]. 2019, 53(4): 65-71.
王树亮 1, 毕大平 1, 张奎 1, 等. 认知雷达波形和检测门限自适应跟踪算法[J]. 西安交通大学学报, 2019,53(4):65-71. DOI： 10.7652/xjtuxb201904010.

An Adaptive Tracking Algorithm Based on Waveform and Detection Threshold of Cognitive Radars[J]. 2019, 53(4): 65-71. DOI： 10.7652/xjtuxb201904010.

摘要

为有效改善杂波背景下雷达目标跟踪中的不确定性问题

提出一种基于认知雷达的波形和检测门限联合自适应跟踪算法。首先

给出雷达目标跟踪的信息熵不确定模型

提出用滤波误差协方差的行列式来描述目标状态跟踪的不确定性; 然后

基于时延-多普勒分辨单元理论

给出单个高斯线性调频脉冲包含波形和虚警概率参数的量测误差协方差的近似表达式; 最后

受人类“感知-行动”循环机制启发

设计出基于信息熵最小为代价函数的自适应波形和检测门限跟踪算法。仿真结果表明

所提出的联合自适应算法的信息熵比目标跟踪的不确定性传统算法可减少30%以上

总体跟踪性能得到明显改善。

Abstract

A joint adaptive tracking algorithm based on waveform and detection threshold of cognitive radars is proposed to effectively improve the uncertainty of radar target tracking in cluttered background. Firstly

an uncertainty model for the information entropy of radar target tracking is given

and the determinant of the covariance of filtering errors is proposed to describe the uncertainty of target state tracking. Then

an approximation of measurement error covariance of a single Gaussian linear frequency modulation(LFM)pulse with waveform and false alarm probability parameters is given based on the theory of time delay-Doppler resolution cell. Finally

inspired by human “perception-action” cycle mechanism

an adaptive waveform and detection threshold tracking algorithm is designed based on minimizing the information entropy as a cost function. Simulation results and a comparison with the traditional algorithm show that

from the point of view of information entropy

the proposed joint adaptive algorithm reduces the uncertainty of target tracking by more than 30%

and the overall tracking performance is significantly improved.

关键词

Keywords

references

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