基于卷积ADMM网络的高效结构化稀疏ISAR成像方法

doi:10.12305/j.issn.1001-506X.2023.01.08

Abstract

Abstract:

Structural sparse inverse synthetic aperture radar (ISAR) imaging is an important approach for situation awareness and object recognition. It can be solved via compressive sensing (CS) methods. At present, many conventional CS algorithms still suffer from low computational efficiency and poor parameter adaptability. In this paper, a structural sparse ISAR imaging method based on convolutional alternating direction method of multipliers network (C-ADMMN) is proposed to overcome those problems. The network is established via deep unfolding methods combined with traditional structural sparse ISAR imaging models. The network only needs approximate 10 layers to achieve the effect of hundreds of iterations in traditional methods through supervised learning. The network achieves higher computing efficiency and has a certain sdaptability to different goals, which is proved on the experiment results based on simulated and measured data.

Key words: inverse synthetic aperture radar (ISAR), compressive sensing (CS), deep learning, deep unfolding

CLC Number:

TN957.52

Ruize LI, Shuanghui ZHANG, Yongxiang LIU. Computational efficient structural sparse ISAR imaging method based on convolutional ADMM-net[J]. Systems Engineering and Electronics, 2023, 45(1): 56-70.

Figures/Tables 29

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Table 1

Fig.11

Fig.12

Fig.13

Fig.14

Table 2

Fig.15

Fig.16

Fig.17

Fig.18

Table 3

Fig.19

Fig.20

Fig.21

Fig.22

Fig.23

Fig.24

Fig.25

Fig.26

References 32

1	DONOHOD L.Compressed sensing[J].IEEE Trans.on Information Theory,2006,52(4):1289-1306. doi: 10.1109/TIT.2006.871582
2	ZHANG L, WU S J, DUAN J. Sparse aperture ISAR imaging of maneuvering targets with sparse representation[C]//Proc. of the IEEE Radar Conference, 2015: 1623-1626.
3	ZENGC Z,ZHUW G,JIAX,et al.Sparse aperture ISAR imaging method based on joint constraints of sparsity and low rank[J].IEEE Trans.on Geoscience and Remote Sensing,2021,59(1):168-181. doi: 10.1109/TGRS.2020.2994179
4	TOMEIS,BACCIA,GIUSTIE M,et al.Compressive sensing-based inverse synthetic radar imaging imaging from incomplete data[J].IET Radar, Sonar and Navigation,2016,10(2):386-397. doi: 10.1049/iet-rsn.2015.0290
5	NAZARIM,MEHRPOOYAA,BASTANIM H,et al.High-dimensional sparse recovery using modified generalised SL0 and its application in 3D ISAR imaging[J].IET Radar, Sonar and Navigation,2020,14(8):1267-1278. doi: 10.1049/iet-rsn.2020.0013
6	BOYDS,PARIKHN,CHUE,et al.Distributed optimization and statistical learning via the alternating direction method of multipliers[J].Foundations & Trends in Machine Learning,2010,3(1):1-122.
7	ZHANGS H,LIUY X,LIX.Computationally efficient sparse aperture ISAR autofocusing and imaging based on fast ADMM[J].IEEE Trans.on Geoscience and Remote Sensing,2020,58(12):8751-8765. doi: 10.1109/TGRS.2020.2990445
8	ZHANGS H,LIUY X,LIX.Fast sparse aperture ISAR autofocusing and imaging via ADMM based sparse Bayesian learning[J].IEEE Trans.on Image Processing,2020,29,3213-3226. doi: 10.1109/TIP.2019.2957939
9	邓理康,张双辉,张弛,等.一种基于多维交替方向乘子法的多输入多输出逆合成孔径雷达成像方法[J].雷达学报,2021,10(3):416-431.
	DENGL K,ZHANGS H,ZHANGC.A MIMO-ISAR imaging method based on multi-dimensional ADMM[J].Journal of Radars,2021,10(3):416-431.
10	FANGJ,SHENY N,LIH B,et al.Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals[J].IEEE Trans.on Signal Processng,2015,63(2):360-372. doi: 10.1109/TSP.2014.2375133
11	WANGL,ZHAOL F,RAHARDJAS,et al.Alternative to extended block sparse bayesian learning and its relation to pattern-coupled sparse bayesian learning[J].IEEE Trans.on Signal Processing,2018,66(10):2759-2771. doi: 10.1109/TSP.2018.2816574
12	刘常浩, 卫扬铠, 王岩, 等. 块稀疏贝叶斯学习双基SAR超分辨率成像[C]//第十四届全国信号和智能信息处理与应用学术会议, 2021: 341-347.
	LIU C H, WEI Y K, WANG Y, et al. Bi-SAR super resolution imaging method based on block sparse Bayesian learning[C]//Proc. of the 14th National Conference on Signal and Intelligent Information Processing and Application, 2021: 341-347.
13	FANGJ,ZHANGL Z,LIH B.Two-dimensional pattern-coupled sparse bayesian learning via generalized approximate message passing[J].IEEE Trans.on Image Processing,2016,25(6):2920-2930. doi: 10.1109/TIP.2016.2556582
14	蒲涛,童宁宁,冯为可,等.基于块稀疏矩阵恢复的MIMO雷达扩展目标高分辨成像算法[J].系统工程与电子技术,2021,43(3):647-655.
	PUT,TONGN N,FENGW K,et al.A MIMO radar high resolution imaging algorithm for distributed target based on block sparse matrix recovery[J].Systems Engineering and Electronics,2021,43(3):647-655.
15	ZHANGX H,BAIT,MENGH Y,et al.Compressive sensing-based ISAR imaging via the combination of the sparsity and nonlocal total variation[J].IEEE Geoscience and Remote Sensing Letters,2014,11(5):990-994. doi: 10.1109/LGRS.2013.2284288
16	薛东方,朱晓秀,胡文华,等.基于加权l₁范数优化的双基地ISAR稀疏成像算法[J].系统工程与电子技术,2021,43(4):944-953.
	XUED F,ZHUX X,HUW H,et al.Bi-ISAR sparse imaging algorithm based on reweighted l₁ norm optimization[J].Systems Engineering and Electronics,2021,43(4):944-953.
17	FUS,ZHONGS S,LINL,et al.A novel time-series memory auto-encoder with sequentially updated reconstructions for remaining useful life prediction[J].IEEE Trans.on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2021.3084249
18	CHENZ,GUOW L,FENGY J,et al.Deep-learned regularization and proximal operator for image compressive sensing[J].IEEE Trans.on Image Processing,2021,30,7112-7126. doi: 10.1109/TIP.2021.3088611
19	XUJ R,YANGY,PUS L,et al.NetRL: task-aware network denoising via deep reinforcement learning[J].IEEE Trans.on Knowledge and Data Engineering, doi: 10.1109/TKDE.2021.3091022
20	SHEIKHOLESLAMIM M,NADIS,NAEINIA A,et al.An efficient deep unsupervised superresolution model for remote sensing images[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2020,13,1937-1945. doi: 10.1109/JSTARS.2020.2984589
21	HERSHEY J R, ROUX J L, WENINGER F. Deep unfolding: model-based inspiration of novel deep architectures[EB/OL]. [2021-07-01]. https://arxiv.org/abs/1409.2574.
22	BORGERDINGM,SCHNITERP,RANGANS.AMP-inspired deep networks for sparse linear inverse problems[J].IEEE Trans.on Signal Processing,2017,65(16):4293-4308. doi: 10.1109/TSP.2017.2708040
23	METZLER C, MOUSAVI A, BARANIUK R. Learned DAMP: principled neural network based compressive image recovery[C]//Proc. of the Advances in Neural Information Processing Systems, 2017: 1772-1783.
24	YANGY,SUNJ,LIH B,et al.ADMM-CSNet: a deep learning approach for image compressive sensing[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,2020,42(3):521-538. doi: 10.1109/TPAMI.2018.2883941
25	LIY Y,CHENGX F,GUIG.Co-robust-ADMM-net: joint ADMM framework and DNN for robust sparse composite regularization[J].IEEE Access,2018,6,47943-47952. doi: 10.1109/ACCESS.2018.2867435
26	WEIX,YANGJ,LVM J,et al.ISAR high-resolution imaging method with joint FISTA and VGGNet[J].IEEE Access,2021,9,86685-86697. doi: 10.1109/ACCESS.2021.3086980
27	WEIS J,LIANGJ D,WANGM,et al.AF-AMPNet: a deep learning approach for sparse aperture ISAR imaging and autofocusing[J].IEEE Trans.on Geoscience and Remote Sensing,2022,60,S206514.
28	HUQ Y,LIUY Z,CAIY L,et al.Joint deep reinforcement learning and unfolding: beam selection and precoding for mmWave multiuser MIMO with lens arrays[J].IEEE Journal on Selected Areas in Communications,2021,39(8):2289-2304. doi: 10.1109/JSAC.2021.3087233
29	汪玲,胡长雨,朱岱寅.基于复数深度神经网络的逆合成孔径雷达成像方法[J].南京航空航天大学学报,2020,52(5):695-700.
	WANGL,HUC Y,ZHUD Y.An ISAR imaging method based on complex-valued deep neural network[J].Journal of Nanjing University of Aeronautics & Astronautics,2020,52(5):695-700.
30	LIR Z,ZHANGS H,ZHANGC,et al.Deep learning approach for sparse aperture isar imaging and autofocusing based on complex-valued ADMM-Net[J].IEEE Sensors Journal,2021,21(3):3437-3451. doi: 10.1109/JSEN.2020.3025053
31	ZHANGS H,LIUY X,LIX,et al.Enhancing ISAR image efficiently via convolutional reweighted l₁ minimization[J].IEEE Trans.on Image Processing,2021,30,4291-4304. doi: 10.1109/TIP.2021.3070442
32	TIBSHIRANIR.Regression shrinkage and selection via the LASSO[J].Journal of the Royal Statistical Society,1996,58(1):267-288.

稀疏度/%	算法	CC	RMSE	PSNR/dB	运算时间/s
50	ADMM^[7]	0.957 0	0.010 2	74.101 7	1.073 2
	CV-ADMMN^[30]	0.979 3	0.006 8	78.467 6	0.343 7
	l₁加权ADMM^[31]	0.975 6	0.007 4	77.464 6	1.846 0
	C-ADMMN	0.979 2	0.006 9	78.626 2	0.069 4
25	ADMM^[7]	0.938 1	0.012 6	72.196 5	4.237 4
	CV-ADMMN^[30]	0.920 9	0.014 3	71.128 7	0.322 9
	l₁加权ADMM^[31]	0.960 9	0.009 5	75.452 0	2.921 9
	C-ADMMN	0.965 5	0.009 0	76.270 0	0.090 0
12.5	ADMM^[7]	0.816 1	0.021 6	66.666 1	3.452 3
	CV-ADMMN^[30]	0.868 3	0.017 5	67.989 5	0.789 1
	l₁加权ADMM^[31]	0.884 9	0.015 7	71.140 7	4.623 7
	C-ADMMN	0.915 1	0.014 27	71.142 2	0.259 8

SNR/dB	算法	CC	RMSE	PSNR/dB	运算时间/s
8	ADMM^[7]	0.935 8	0.012 9	71.809 5	1.226 0
	CV-ADMMN^[30]	0.941 1	0.011 8	72.489 5	0.649 3
	l₁加权ADMM^[31]	0.940 3	0.011 9	73.40 2	3.776 5
	C-ADMMN	0.954 3	0.010 3	74.721 0	0.085 8
0	ADMM^[7]	0.888 0	0.017 0	68.914 7	2.252 8
	CV-ADMMN^[30]	0.898 8	0.016 1	68.762 3	0.675 2
	l₁加权ADMM^[31]	0.937 7	0.012 0	73.768 9	3.466 8
	C-ADMMN	0.947 1	0.011 0	74.561 0	0.069 4
-8	ADMM^[7]	0.708 5	0.022 2	67.179 3	1.538 2
	CV-ADMMN^[30]	0.708 1	0.022 2	67.118 1	0.999 4
	l₁加权ADMM^[31]	0.822 0	0.018 7	71.626 2	5.072 9
	C-ADMMN	0.863 4	0.015 4	72.786 4	0.303 8

稀疏度	算法	CC	RMSE	PSNR/dB	运算时间/s
50%	ADMM^[7]	0.884 0	0.006 2	62.348 1	1.232 1
	CV-ADMMN^[30]	0.915 4	0.005 6	63.136 1	0.676 5
	l₁加权ADMM^[31]	0.909 6	0.005 4	63.921 6	7.915 9
	C-ADMMN	0.948 7	0.004 1	66.186 6	0.172 2
25%	ADMM^[7]	0.861 9	0.006 8	61.561 7	2.918 0
	CV-ADMMN^[30]	0.890 6	0.006 3	61.976 2	0.894 3
	l₁加权ADMM^[31]	0.867 6	0.006 5	61.842 1	7.714 1
	C-ADMMN	0.925 9	0.004 9	64.716 0	0.285 5

[1]	Xiao HAN, Shiwen CHEN, Meng CHEN, Jincheng YANG. Open-set recognition of LPI radar signal based on reciprocal point learning [J]. Systems Engineering and Electronics, 2022, 44(9): 2752-2759.
[2]	Limin ZHANG, Kaiwen TAN, Wenjun YAN, Yuyuan ZHANG. Radar emitter recognition based on multi-level jumper residual network [J]. Systems Engineering and Electronics, 2022, 44(7): 2148-2156.
[3]	Guodong JIN, Yuanliang XUE, Lining TAN, Jiankun XU. Advances in object tracking algorithm based on siamese network [J]. Systems Engineering and Electronics, 2022, 44(6): 1805-1822.
[4]	Anlin XU, Yu ZHANG, Feng ZHOU. High resolution ISAR imaging based on Beta process [J]. Systems Engineering and Electronics, 2022, 44(6): 1873-1879.
[5]	Xiaofeng ZHAO, Yebin XU, Fei WU, Jiahui NIU, Wei CAI, Zhili ZHANG. Ground infrared target detection method based on global sensing mechanism [J]. Systems Engineering and Electronics, 2022, 44(5): 1461-1467.
[6]	Hong ZOU, Chenyang BAI, Peng HE, Yaping CUI, Ruyan WANG, Dapeng WU. Edge service placement strategy based on distributed deep learning [J]. Systems Engineering and Electronics, 2022, 44(5): 1728-1737.
[7]	Fengkai LIU, Darong HUANG, Xinrong GUO, Cunqian FENG. Parametric translational compensation method for maneuvering target based on Lv's distribution [J]. Systems Engineering and Electronics, 2022, 44(4): 1166-1173.
[8]	Dong CHEN, Yanwei JU. Ship object detection SAR images based on semantic segmentation [J]. Systems Engineering and Electronics, 2022, 44(4): 1195-1201.
[9]	Jingming SUN, Shengkang YU, Jun SUN. Pose sensitivity analysis of HRRP recognition based on deep learning [J]. Systems Engineering and Electronics, 2022, 44(3): 802-807.
[10]	Yunxiang YAO, Ying CHEN. Target tracking network based on dual-modal interactive fusion under attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(2): 410-419.
[11]	Yongxing GAO, Xudong WANG, Ling WANG, Daiyin ZHU, Jun GUO, Fanwang MENG. Weather signal detection for dual polarization weather radar based on RCNN [J]. Systems Engineering and Electronics, 2022, 44(11): 3380-3387.
[12]	Yiheng ZHOU, Jun YANG, Saiqiang XIA, Mingjiu LYU. Estimation method of micro-motion parameters for rotor targets under flashing [J]. Systems Engineering and Electronics, 2022, 44(1): 54-63.
[13]	Rong FU, Tianyao HUANG, Yimin LIU. DNN based 1-bit block sparse recovery in frequency agile coherent radar [J]. Systems Engineering and Electronics, 2022, 44(1): 70-75.
[14]	Feng RUAN, Liang GUO, Yachao LI, Ran XU. Phased array radar forward-looking imaging based on correlated imaging [J]. Systems Engineering and Electronics, 2021, 43(9): 2457-2462.
[15]	Yuyuan ZHANG, Limin ZHANG, Wenjun YAN. SFBC-OFDM recognition method based on cross-correlation feature map and dilated dense convolutional neural networks [J]. Systems Engineering and Electronics, 2021, 43(9): 2657-2664.

Computational efficient structural sparse ISAR imaging method based on convolutional ADMM-net

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 29

References 32

Related Articles 15

Recommended Articles

Metrics

Comments