Fuze Target Recognition Method Based on Motion Array Microwave Imaging and Multi-scale Deformable Convolutional Network

doi:10.12382/bgxb.2024.0282

Abstract

Abstract:

In response to the challenge of inadequate target recognition capabilities due to the limited detection dimension and weak azimuth resolution of conventional frequency modulated continuous wave (FMCW) fuze,a fuze target recognition method based on motion array microwave imaging and multi-scale deformable convolutional networks (MSDCN) is proposed.A FMCW motion array antenna model is established thorough analysis of the thorough analysis of echo phase variation during the fuze motion.The virtual array elements of fuze antenna are expanded by motion synthesis to significantly enhance the azimuth resolution of the fuze,thus achieving the two-dimensional high-resolution imaging of target distance and azimuth.Simultaneously,a MSDCN target recognition model is constructed by delving into the multi-scale characteristics of the images formed due to the variations in target position,attitude,distance,and other states during the fuze-target encounter process.This enhances the adaptive recognition capability of fuze for the multi-scale characteristics of target imaging in complex dynamic encounter scenarios.The experimental results demonstrate that the proposed method significantly enhances the azimuth resolution of fuze.It achieves satisfactory imaging and recognition results in various target scenarios.The accuracy of multi-scale image recognition for typical targets reaches 94%,and even at -6dB signal-to-noise ratio,the target recognition accuracy remains at 88%.

Key words: fuze, frequency-modulated continuous wave, motion array antenna, range-azimuth two-dimensional image, multi-scale deformable convolutional network, target recognition

CLC Number:

TJ434.1

HAN Yanwen, YAN Xiaopeng, GAO Xiaofeng, YI Guanghua, DAI Jian. Fuze Target Recognition Method Based on Motion Array Microwave Imaging and Multi-scale Deformable Convolutional Network[J]. Acta Armamentarii, 2025, 46(3): 240282-.

Figures/Tables 17

References 23

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SNR /dB	MSDCN	DCN	U-Net	ResNet-18	Dense Net-21
-14	0.19	0.18	0.19	0.19	0.2
-12	0.21	0.22	0.23	0.22	0.25
-10	0.35	0.29	0.31	0.3	0.26
-8	0.65	0.49	0.62	0.64	0.65
-6	0.88	0.8	0.82	0.87	0.85
-4	0.92	0.85	0.89	0.9	0.9
-2	0.93	0.9	0.92	0.93	0.91
0	0.94	0.92	0.93	0.93	0.93
2	0.95	0.94	0.94	0.93	0.95
4	0.95	0.93	0.94	0.94	0.95

SNR /dB	MSDCN	DCN	U-Net	ResNet-18	Dense Net-21
-14	0.19	0.18	0.19	0.19	0.2
-12	0.21	0.22	0.23	0.22	0.25
-10	0.35	0.29	0.31	0.3	0.26
-8	0.65	0.49	0.62	0.64	0.65
-6	0.88	0.8	0.82	0.87	0.85
-4	0.92	0.85	0.89	0.9	0.9
-2	0.93	0.9	0.92	0.93	0.91
0	0.94	0.92	0.93	0.93	0.93
2	0.95	0.94	0.94	0.93	0.95
4	0.95	0.93	0.94	0.94	0.95