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In the field of radar target detection and recognition, jamming technology is constantly upgraded. In order to solve the problem that traditional method of migration inadequate adaptive in facing sea surface target detection task under complex scene environment, a target detection and recognition method of radar echo signal based on deep learning is proposed. By dissecting the data characteristics of radar seeker data, the difference between radar echo and visible light data is compared, and the radar echo date of sea surface ships and jamming targets are analyzed experimentally. The target detection and recognition deep neural network model, which is originally suitable for visible light data domain, is transferred to the radar echo data domain. Lightweight model experiment is carried out to facilitate the embedded development work.Finally, the model training and algorithm validation are carried out on relevant radar echo data set, and excellent results are obtained, which verifies the feasibility and availability of the deep neural network model in radar data field.
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Basic Information:
DOI：10.16358/j.issn.1009-1300.2021.9.114
China Classification Code:TN957.51;TP18
Citation Information:
[1]Song Hailing,Sun Yuhang,He Liang ,et al.Research on Deep Learning Target Detection and Recognition Method Based on Radar Echo Signal[J].Tactical Missile Technology,2021,No.206(02):117-126.DOI:10.16358/j.issn.1009-1300.2021.9.114.