Identification of sea corner reflector array based on spatial morphological features

Dawei Duan; Yang Shen; Zhongjie Wu; Jianping Ou

doi:10.1117/12.3012089

21 December 2023 Identification of sea corner reflector array based on spatial morphological features

Dawei Duan, Yang Shen, Zhongjie Wu, Jianping Ou

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129701L (2023) https://doi.org/10.1117/12.3012089
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Corner reflector arrays are a crucial passive interference against anti-ship radar guides. High resolution range profile is a projected distribution of the electromagnetic scattering properties of a target in one dimension. Corner reflectors can be placed in various ways to form a variety of corner reflector arrays to approximate the range profile of a ship. Still, in practice, the physical dimensions of ships and corner reflector arrays vary greatly. However, the physical sizes of ships and angular reflector arrays differ considerably. In this paper, based on the principle of monopulse 3D imaging, using the three-dimensional scattering center model of ships and corner reflector array, we propose the minimum volume enclosing ellipsoid feature that can fully characterize the spatial morphological information of the target and discriminate the ships and angular counter arrays by SVM classifiers. The experimental results show that the proposed method in this paper has an identification accuracy of more than 93% for ships and corner reflector arrays at SCR10dB and more than 80% when the sea surface height is below 6m.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dawei Duan, Yang Shen, Zhongjie Wu, and Jianping Ou "Identification of sea corner reflector array based on spatial morphological features", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129701L (21 December 2023); https://doi.org/10.1117/12.3012089

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