中国海洋大学学报（自然科学版）2024，Vol.54Issue(1)：104-112,9.DOI:10.16441/j.cnki.hdxb.20220456
基于差值信噪比法的船载地波雷达一阶谱识别
Identification of the First-Order Spectral for Ship-Borne HFSWR Based on Difference Signal-to-Noise Ratio Method
摘要
Abstract
For ship-borne high frequency surface wave radar(HFSWR),the identification and extrac-tion of the first-order spectral is the key step of sea state inversion and sea clutter suppression in target monitoring.However,due to the motion of the ship-borne platform,the first-order spectrum of the ship-borne HFSWR is broadened,which makes it difficult to be accurately identified.In this paper,a difference signal-to-noise ratio(DSNR)method for ship-borne HFSWR broadened first-order Doppler spectra extraction in the complex scene of ship-borne platform movement and multi-target signal inter-ference is proposed.Firstly,combining the simulation analysis and the measured test data,the frequen-cy shift and broadening characteristics of the first-order spectrum of the ship-borne HFSWR are ana-lyzed,and the quantitative relationship between the maximum broadening range of the first-order spec-trum and the movement speed of the platform is determined.Secondly,the boundary prescreening crite-rion is determined by analyzing the difference quantity relationship between adjacent frequency points in Doppler spectra.Then,using the region continuity of the broadened first-order spectrum,the boundary of the broadened first-order spectrum is finally recognized.At last,the effectiveness and applicability of the method is verified by ship-borne HFSWR simulation and measured data.And the results show that the proposed method has a greater improvement in the ability of recognition compared with traditional difference spectrum method.关键词
高频地波雷达/一阶谱/差值信噪比/特性分析Key words
high frequency surface wave radar/first-order spectrum/difference signal-to-noise ratio/characteristic analysis分类
信息技术与安全科学引用本文复制引用
姜美成,纪永刚,程啸宇,任继红,梁旭,孙伟峰..基于差值信噪比法的船载地波雷达一阶谱识别[J].中国海洋大学学报（自然科学版）,2024,54(1):104-112,9.基金项目
国家自然科学基金项目(62271507,62031015) （62271507,62031015）
山东省自然科学基金项目(ZR202112010167)资助 Supported by the National Natural Science Foundation of China(62271507,62031015) （ZR202112010167）
the Natural Science Foundation of Shandong Province(ZR202112010167) （ZR202112010167）
