基于零空间l1范数最小化的ISAR成像方法

doi:10.3969/j.issn.1001-506X.2020.02.09

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (2): 315-321.doi: 10.3969/j.issn.1001-506X.2020.02.09

基于零空间l₁范数最小化的ISAR成像方法

徐楚¹(), 朱栋强²(), 汪玲¹(), 王洁²()

1. 南京航空航天大学雷达成像与微波光子技术教育部重点实验室, 南京江苏 210016
2. 上海无线电设备研究所, 上海 201109

收稿日期:2019-04-02 出版日期:2020-02-01 发布日期:2020-01-23
作者简介:徐楚(1994-),男,硕士,主要研究方向为ISAR成像。E-mail:xuchujs1994@163.com|朱栋强(1993-),男,硕士,主要研究方向为基于压缩感知的ISAR成像。E-mail:1298370073@qq.com|汪玲(1977-),女,教授,博士,主要研究方向为逆散射理论、图像重建、波成像、合成孔径成像、无源成像、雷达、统计信号处理等。E-mail:wanglrpizess@163.com|王洁(1993-),女,硕士,主要研究方向为压缩感知无源成像。E-mail:313807132@qq.com
基金资助:
国家自然科学基金(61871217)

ISAR imaging using null space l₁ norm minimization

Chu XU¹(), Dongqiang ZHU²(), Ling WANG¹(), Jie WANG²()

1. Key Laboratory of Radar Imaging and Microwave Photonics of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2. Shanghai Radio Equipment Research Institute, Shanghai 201109, China

Received:2019-04-02 Online:2020-02-01 Published:2020-01-23
Supported by:
国家自然科学基金(61871217)

摘要/Abstract

摘要：

在目标场景散射率分布满足稀疏性假设下,压缩感知(compressive sensing, CS)成像与传统距离-多普勒成像方法相比,可以使用很少的数据获得良好的图像,图像对比度高,没有旁瓣干扰。本文提出了一种基于零空间l₁范数最小化的逆合成孔径雷达(inverse synthetic aperture radar, ISAR) CS成像方法。从解欠定方程组的角度,将待重建目标图像分解为初猜值与残余值两部分。首先使用加权最小二乘(weighted lease square, WLS)法估计初猜值,作为目标初像；然后将待重建目标场景散射率的l₁范数作为额外的一个非线性测量值引入到图像重建中,在卡尔曼滤波框架下,利用非线性“伪测量”值,最小化待重建目标场景的l₁范数来估计零空间中残余值的解。实测ISAR数据处理验证了所提算法的有效性。与正交匹配追踪算法(matching pursuit algorithm, OMP)和primal-dual l₁范数最小化方法相比,所提方法获得的成像效果更好,成像时间比primal-dual l₁范数最小化方法更短。

关键词: 逆合成孔径雷达, 成像, 压缩感知, l₁范数, 零空间

Abstract:

Under the assumption that the target scene is sparse, the compressive sensing (CS) imaging can use very few data to obtain good images with high contrast and no side-lobe interference as compared with the conventional range-Doppler imaging methods. In this paper, an inverse synthetic aperture radar (ISAR) CS imaging method based on the minimization of the null-space l₁ norm is proposed. The solution of the underdetermined linear imaging system is decomposed into two parts: the preliminary value and the residual value. First, the weighted lease square method is used to estimate the preliminary value, which is used as the target initial image. Then, the l₁ norm of the target scene reflectivity is introduced as an additional non-linear measurement and used in the image reconstruction. Within the Kalman filter framework, the residual value in the null space is estimated by minimizing the l₁ norm of the target scene using the nonlinear pseudo-measurement. The ISAR real data processing verifies the effectiveness of the proposed method. The image quality obtained by the proposed method is better than that of the orthogonal matching pursuit algorithm (OMP) and the primal-dual l₁ norm minimization method. The imaging time is much less than the primal-dual l₁ norm minimization method and comparable to OMP.

Key words: inverse synthetic aperture radar (ISAR), imaging, compressive sensing (CS), l₁ norm, null space

中图分类号:

TN957.52

徐楚, 朱栋强, 汪玲, 王洁. 基于零空间l₁范数最小化的ISAR成像方法[J]. 系统工程与电子技术, 2020, 42(2): 315-321.

Chu XU, Dongqiang ZHU, Ling WANG, Jie WANG. ISAR imaging using null space l₁ norm minimization[J]. Systems Engineering and Electronics, 2020, 42(2): 315-321.

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数据集	原数据	测量值
飞机目标	100×80	4 047(50%)
舰船目标	80×100	4 047(50%)

方法	测量数据/%	RRMSE	TCR	ENT	程序运行时间/s
传统RD			18.012 0	5.562 1
本文方法1	4 047(50)	0.163 9	28.329 2	5.271 1	2 654.32
本文方法2	4 047(50)	0.187 4	26.990 1	5.250 1	463.27
传统OMP	4 047(50)	0.278 9	21.003 7	5.743 2	655.10
primal-dual l₁范数最小化	4 047(50)	0.234 5	23.874 1	5.314 0	3 421.23

方法	测量数据/%	RRMSE	TCR	ENT	程序运行时间/s
传统RD			24.492 1	4.867 2
本文方法1	4 047(50)	0.154 6	30.121 7	4.777 8	3 276.43
本文方法2	4 047(50)	0.164 9	29.942 2	4.756 8	527.25
传统OMP	4 047(50)	0.337 1	23.463 7	5.098 5	495.45
primal-dual l₁范数最小化	4 047(50)	0.253 0	25.432 1	5.035 0	4 790.34

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基于零空间l₁范数最小化的ISAR成像方法

ISAR imaging using null space l₁ norm minimization

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