结合迁移学习基于全卷积神经网络的ISAR自聚焦算法

doi:10.7527/S1000-6893.2023.28172

Abstract

Abstract:

The targets of Inverse Synthetic Aperture Radar （ISAR） imaging are mostly non-cooperative， and motion compensation is the key of ISAR imaging when the relative motion status of target is unknown. Fully Convolutional Network （FCN） has excellent feature extraction and fitting abilities， and an ISAR phase compensation algorithm based on FCN and Transfer Learning is proposed by utilizing the FCN in ISAR autofocusing in this paper. Firstly， a large number of simulation data sets with different phase errors are constructed and trained to provide phase compensation ability for FCN. In addition， the transfer training of simulation and measured data with different attitudes is carried out to improve the translational phase compensation ability. The measured data processing results show that the performance of FCN phase compensation algorithm is better than traditional algorithm， which verifies the effectiveness in the proposed method and the superiority compared with the traditional method.

Key words: inverse synthetic aperture radar, imaging, phase compensation, fully convolutional neural network, transfer learning

CLC Number:

V243.2

Lianzi WANG, Ling WANG, Daiyin ZHU. An ISAR autofocus imaging algorithm based on FCN and transfer learing[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(17): 328172-328172.

Figures/Tables 12

Fig.1

Fig. 2

Fig.3

Fig.4

Table 1

Radar parameters

参数	符号	数值
载频	$f 0$	10 GHz
带宽	B	300 MHz
脉冲宽度	$T p$	$1 μ s$
脉冲重复频率	PRF	100 Hz
积累脉冲	$N a$	300

Table 1

Table 2

Fig.5

Fig.6

Table 3

Table 4

Table 5

Table 6

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超参数	学习率	学习率策略	迭代次数	优化策略
姿态1	0.000 01	固定	150	SGD
姿态2	0.000 01	固定	50	SGD

指标	ENT	IC	FA	MD	RRMSE	TCR
初像	7.882	3.338	808	14	1.311	27.182
MEA	7.024	6.097	86	29	0.519	31.606
PGA	6.773	7.762	57	29	0.474	33.564
CNN	6.191	11.22	10	12	0.196	67.075
FCN	6.556	8.715	3	2	0.120	68.336

指标	ENT	IC	FA	MD	RRMSE	TCR
初像	7.894	3.185	982	9	1.471	26.912
MEA	6.871	6.182	55	21	0.705	37.955
PGA	6.592	8.090	55	21	0.602	41.676
CNN	5.574	11.22	95	21	1.253	46.457
FCN	6.479	8.341	25	6	0.507	45.988

指标	ENT	IC	FA	MD	RRMSE	TCR
初像	6.309	7.745	257	106	0.980 9	44.913
MEA	6.181	8.913
PGA	6.160	9.174
CNN	6.521	8.608	156	117	0.613	51.645
FCN	5.260	11.202	74	110	0.549	57.392

指标	ENT	IC	FA	MD	RRMSE	TCR
初像	5.320	14.527	58	56	0.721	60.242
MEA	4.844	18.002
PGA	5.167	15.489
CNN	5.669	9.416	182	38	0.976	48.655
FCN	4.163	20.239	4	54	0.327	85.591

An ISAR autofocus imaging algorithm based on FCN and transfer learing

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