基于卷积神经网络的海面目标全极化高分辨距离像识别技术

doi:10.12305/j.issn.1001-506X.2022.01.15

Abstract

Abstract:

Aiming at the problem that when extracting separability features from radar target full polarization high resolution range profile (HRRP), using all range units as the measurement scale cannot retain the specific characteristics of each range unit, a single range unit is selected as the measurement scale when comprehensively using the ship target HRRP information of four polarization channels. On this basis, a feature extraction method based on Pauli decomposition, H、α、A、α₁ decomposition and structural similarity parameters is proposed to extract the features of the target polarization scattering matrix, and the extracted features are combined with the ship target HRRP recognition method based on convolutional neural network (CNN). The improved residual structure CNN is used to further extract deep separability features from polarization features for target recognition. Experimental results show that the proposed method can retain more features of target full polarization HRRP and improve the accuracy of target recognition.

Key words: convolutional neural network (CNN), full-polarization high resolution range profile (HRRP), separable characteristics, Pauli decomposition

CLC Number:

TN957.51

Bo DAN, Zhequan FU, Shan GAO, Tao JIAN. Full-polarization high resolution range profile recognition technology for sea surface target based on convolutional neural network[J]. Systems Engineering and Electronics, 2022, 44(1): 108-116.

Figures/Tables 14

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参数	取值
中心频率/GHz	10
带宽/MHz	80
采样点数	256
方位角范围/(°)	0~360
方位角间隔/(°)	1
俯仰角/(°)	80
距离单元数	256
分辨单元长度/m	1.875

阶段	输出维度	参数个数
初始卷积层	128×1×9	63+36
卷积模块1	64×1×18	405+180
卷积模块2	32×1×36	1 620+360
卷积模块3	16×1×72	6 480+720
卷积模块4	8×1×144	25 920+1 440
全连接层1	144	0
全连接层2	2	288
输出层	3	6

数据集	参数总数	模型大小/kB	模型复杂度	运算时间/μs	平均识别准确率/%
Data-HH	37 518	772	190 123	155	83.32
Data-HV	37 518	772	190 123	157	80.16
Data-VH	37 518	772	190 123	163	80.10
Data-VV	37 518	772	190 123	161	83.67
Data-HHHVVV	51 414	938	251 017	98	89.86
Data-Pauli	51 414	938	251 017	98	90.08
Data-Full	51 414	938	251 017	106	91.56
Data-Full+Pauli	51 414	938	251 017	112	92.41
Data-Full+H, α, A, α₁	51 414	938	251 017	125	93.05
Data-Full+Similarity	51 414	938	251 017	126	93.74

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Full-polarization high resolution range profile recognition technology for sea surface target based on convolutional neural network

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