基于时频分形特征的雨杂波环境目标检测算法
Rain clutter environment target detection algorithm based on time-frequency fractal features
钟林茂 1尤鹏杰 2王琳 2王海涛1
作者信息
- 1. 桂林电子科技大学信息与通信学院 桂林 541002
- 2. 中国电子科技集团公司第五十四研究所 石家庄 050081
- 折叠
摘要
针对雨杂波环境下传统目标检测方法虚警和漏警双高的问题,本文主要研究雨杂波频谱的联合分形特征及其在目标检测中的应用,提出了一种基于定向毯子覆盖法的联合分形特征检测方法,首先通过利用毯子覆盖法测量回波距离多普勒域的分形维数和模型拟合误差特征,然后将分形维数和模型拟合误差作为检验统计值,构造基于联合特征的阈值检测方法.通过优化毯子法的计算步骤,减少了对非目标信息的冗余运算,使得方法具有更好的实时性能.通过对雨杂波环境中实测数据的处理结果表明,相比于传统的目标检测算法,该方法在处理雨杂波等非平稳数据时,可以有效降低虚警,同时提升对目标的检测性能.
Abstract
In view of the issue of high false alarms and missed detections in traditional target detection methods under rainy clutter environments,this paper primarily investigates the joint fractal characteristics of rain clutter spectra and their application in target detection.We propose a joint fractal feature detection method based on the directional blanket covering method has been proposed.Firstly,the fractal dimension and model fitting error features of the echo's distance-Doppler domain are measured using the blanket covering method.Subsequently,these fractal dimension and model fitting error features are employed as verification statistics to construct a threshold-based detection method with combined features.By optimizing the computational steps of the blanket method,redundant calculations on non-target information are reduced,thereby enhancing the real-time performance of the method.Based on the processing results of the measured data in rainy and cluttered environments,the method demonstrates a significant reduction in false alarms and an improved detection performance for targets compared to traditional target detection algorithms when handling non-stationary data such as rain clutter.
关键词
目标检测/毯子法/联合分形/雨杂波Key words
target detection/blanket method/joint fractal/rain clutter引用本文复制引用
基金项目
广西创新驱动发展专项(桂科AA21077008)
广西高校中青年教师科研基础能力提升项目(2021KY0197)
出版年
2024
NETL
NSTL
万方数据