基于加权l1范数优化的双基地ISAR稀疏成像算法

doi:10.12305/j.issn.1001-506X.2021.04.11

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (4): 944-953.doi: 10.12305/j.issn.1001-506X.2021.04.11

基于加权l₁范数优化的双基地ISAR稀疏成像算法

薛东方(), 朱晓秀*(), 胡文华(), 郭宝锋(), 曾慧燕()

陆军工程大学石家庄校区, 河北石家庄 050003

收稿日期:2020-06-05 出版日期:2021-03-25 发布日期:2021-03-31
通讯作者: 朱晓秀 E-mail:dongfang_25@163.com;zhuxiaoxiu13@163.com;hwhsaq@sina.com;15132497492@126.com;15200011917@126.com
作者简介:薛东方 (1983-), 男, 讲师, 硕士, 主要研究方向为雷达信号处理、雷达故障检测技术。E-mail: dongfang_25@163.com|朱晓秀 (1993-), 女, 博士研究生, 主要研究方向为雷达信号处理、雷达成像技术。E-mail: zhuxiaoxiu13@163.com|胡文华 (1970-), 男, 副教授, 博士, 主要研究方向为雷达信号处理、雷达故障检测技术。E-mail: hwhsaq@sina.com|郭宝锋 (1987-), 男, 讲师, 博士, 主要研究方向为雷达信号处理、雷达成像技术。E-mail: 15132497492@126.com|曾慧燕 (1986-), 女, 助教, 硕士, 主要研究方向为通信与信息系统、数字信号处理。E-mail: 15200011917@126.com
基金资助:
国家自然科学基金(61601496);河北省自然科学基金(F2019506031)

Bi-ISAR imaging based on weighted l₁ norm optimization algorithm

Dongfang XUE(), Xiaoxiu ZHU*(), Wenhua HU(), Baofeng GUO(), Huiyan ZENG()

Shijiazhuang Campus of the Army Engineering University, Shijiazhuang 050003, China

Received:2020-06-05 Online:2021-03-25 Published:2021-03-31
Contact: Xiaoxiu ZHU E-mail:dongfang_25@163.com;zhuxiaoxiu13@163.com;hwhsaq@sina.com;15132497492@126.com;15200011917@126.com

摘要/Abstract

摘要：

针对低信噪比条件下实现双基地逆合成孔径雷达(inverse synthetic aperture radar, ISAR)稀疏孔径成像时重构质量较差的问题, 提出了一种基于加权l₁范数优化的高分辨成像算法。首先, 假设各像元稀疏非同分布, 利用贝叶斯准则和最大后验概率估计将双基地ISAR稀疏孔径成像问题转化为加权l₁范数约束问题, 建立成像模型; 然后, 利用柯西-牛顿算法进行加权l₁范数约束最优化问题的求解, 实现目标图像重构。由于假设各像元独立非同分布, 故通过像元加权的方式更好地利用了目标的能量聚集和结构特性, 提高了成像质量。最后, 仿真实验验证了算法的有效性和优越性。

关键词: 双基地逆合成孔径雷达, 稀疏孔径, 加权l₁范数, 压缩感知, 优化理论

Abstract:

To solve the problem of poor reconstruction quality in bistatic inverse synthetic aperture radar (ISAR) sparse aperture imaging under low signal-to-noise ratio conditions, a high resolution imaging algorithm based on weighted l₁ norm optimization is proposed. First, assuming that the image pixels are sparsely distributed, the Bayesian criterion and the maximum a posteriori probability estimation are used to transform the bistatic ISAR sparse aperture imaging problem into a weighted l₁ norm constraint problem, and the imaging model is established. Second, the Cauchy-Newton algorithm is used to solve the weighted l₁ norm constrained optimization problem and obtain the target image reconstruction. Because the pixels are assumed to be independent and non-uniformly distributed, the energy aggregation and structural characteristics of the target are better utilized in the way of weighting, which improves the imaging quality. Finally, simulation experiments verify the effectiveness and superiority of the algorithm.

Key words: bistatic inverse synthetic aperture radar (ISAR), sparse apertures, weighted l₁ norm, compressive sensing, optimization theory

中图分类号:

薛东方, 朱晓秀, 胡文华, 郭宝锋, 曾慧燕. 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.

Dongfang XUE, Xiaoxiu ZHU, Wenhua HU, Baofeng GUO, Huiyan ZENG. Bi-ISAR imaging based on weighted l₁ norm optimization algorithm[J]. Systems Engineering and Electronics, 2021, 43(4): 944-953.

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参考文献 27

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RMES	SNR/dB
RMES	0	10	20
CoSaMP算法	0.194 1	0.143 5	0.103 2
l₁范数	0.152 0	0.095 6	0.061 2
加权l₁范数	0.064 5	0.060 7	0.057 4

参数名称	参数值	参数名称	参数值
载频/GHz	10	累计脉冲个数/个	500
带宽/MHz	800	累积转角/(°)	3.44
采样率/GHz	1	平均双基地角/(°)	42.45
脉冲宽度/μs	20	距离向分辨率/m	0.160 9
脉冲重复频率/Hz	50	方位向分辨率/m	0.268 3

算法	图像衡量标准	SNR/dB
算法	图像衡量标准	20	10	0
CoSaMP算法	C	30.614 1	27.174 4	23.491 7
	TBR	34.942 1	32.697 9	20.038 6
	RMSE	10.974 4	12.216 8	13.724 7
l₁范数算法	C	36.783 5	29.432 5	26.342 6
	TBR	67.423 5	47.388 1	28.372 5
	RMSE	10.686 3	11.454 3	12.054 3
本文算法	C	44.379 7	42.366 7	41.094 8
	TBR	95.238 1	92.434 7	80.656 6
	RMSE	10.324 6	10.551 4	10.845 4

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基于加权l₁范数优化的双基地ISAR稀疏成像算法

Bi-ISAR imaging based on weighted l₁ norm optimization algorithm

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