Sea-surface weak target detection based on multi-feature information fusion

被引：0
|
作者：
Xue C. [1 ]
Cao F. [1 ]
Sun Q. [2 ]
Qin J. [1 ]
Feng X. [1 ]
机构：
[1] Nuclear Engineering College, Rocket Force University of Engineering, Xi'an
[2] School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 11期
关键词：
information fusion; sea clutter; support vector machine (SVM); target detection;
D O I：
10.12305/j.issn.1001-506X.2022.11.07
中图分类号：
学科分类号：
摘要：
To improve the performance of radar weak target detection in sea clutter, a target detection method based on multi-feature information fusion is proposed. Firstly, on the basis of the analysis of time-domain returned signal, the pulse and amplitude deviation rate is defined to characterize the sharpness of discrete returned signal. Secondly, the multi-feature information fusion tensor is constructed by combining the frequency peak to average ratio and local grade of fractality of the returned signal. Thirdly, the support vectors machine (SVM) classifier is trained by cross validation, and the target is detected according to the classifier. Finally, by a series of experimental analysis of the measured sea clutter data, the parameters of the proposed scheme are optimized. Furthermore, the results show that the proposed method has better robustness compared with the existing traditional methods. © 2022 Chinese Institute of Electronics. All rights reserved.
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页码：3338 / 3345
页数：7
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