信号处理2025，Vol.41Issue(6)：969-992,24.DOI:10.12466/xhcl.2025.06.001
雷达海上目标检测技术研究进展
Research Progress in Radar Maritime Target Detection Technology
摘要
Abstract
This paper systematically reviews the advancements in radar maritime target detection technology,focusing on joint and cascaded processing methods in two key areas:the improvement of the signal-to-clutter-and-noise ratio(SCNR)and the formation of detection statistics.For joint processing,the energy-domain adaptive constant false alarm rate(CFAR)detection technologies used for various combinations of different target models,clutter models,test crite-ria,as well as different parameter estimation and training sample screening methods were considered.From the perspec-tive of engineering applications,further in-depth research is needed in three areas:the construction of detection statis-tics in complex situations,the estimation of clutter covariance matrix when the training samples were insufficient,and the improvement of the information utilization rate of data.For cascaded processing,the emphasis was on radar target feature detection methods.A general framework for studying radar target feature detection methods was proposed,and the five steps involved—feature extraction,feature analysis,feature re-expression,feature selection and formation of detection statistics,and solving the detection threshold or decision-making space—were elaborated.Additionally,the formation of detection statistics based on information geometry was described and several main matrix CFAR detectors based on information geometry were listed.Finally,for integrating data-driven and model-driven approaches,three deep learning-based radar target detection processing frameworks were proposed:(1)intelligent identification of obser-vation conditions and selection of model-based maritime target detection algorithms,(2)intelligent processing to re-place traditional radar signal processing links,and(3)end-to-end intelligent integrated processing.This paper also pres-ents suggestions,solutions,and practical results addressing key challenges in radar maritime target detection technology.关键词
雷达目标检测/海上目标/自适应恒虚警/特征检测/信息几何/深度学习Key words
radar target detection/maritime target/adaptive constant false alarm rate/feature detection/information geometry/deep learning分类
信息技术与安全科学引用本文复制引用
何友,黄勇,关键,刘宁波..雷达海上目标检测技术研究进展[J].信号处理,2025,41(6):969-992,24.基金项目
国家自然科学基金卓越研究群体项目(6238810003) （6238810003）
国家自然科学基金(62222120) The National Natural Science Foundation of China Excellent Research Group Project(6238810003) （62222120）
The National Natural Science Foundation of China(62222120) （62222120）
