基于近似观测和最小熵约束的SAR稀疏自聚焦方法

doi:10.12305/j.issn.1001-506X.2021.10.13

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (10): 2803-2811.doi: 10.12305/j.issn.1001-506X.2021.10.13

基于近似观测和最小熵约束的SAR稀疏自聚焦方法

熊旭颖, 李根, 马彦恒*

陆军工程大学石家庄校区无人机工程系, 河北石家庄 050003

收稿日期:2020-08-14 出版日期:2021-10-01 发布日期:2021-11-04
通讯作者: 马彦恒
作者简介:熊旭颖(1998—), 女, 硕士研究生, 主要研究方向为雷达信号处理和曲线轨迹SAR成像|李根(1991—), 男, 博士研究生, 主要研究方向为SAR、压缩感知和曲线轨迹SAR成像|马彦恒(1968—), 男, 教授, 博士, 主要研究方向为机动SAR成像、低小慢目标探测

SAR sparse autofocusing method based on approximate observation and minimum entropy constraint

Xuying XIONG, Gen LI, Yanheng MA*

Department of UAV Engineering, Army Engineering University Shijiazhuang Campus, Shijiazhuang, 050003, China

Received:2020-08-14 Online:2021-10-01 Published:2021-11-04
Contact: Yanheng MA

摘要/Abstract

摘要：

当采样率较低以及重建散射点数量较多时, 基于两步优化的稀疏自聚焦方法收敛速度较慢且容易陷入误差较大的局部最优解, 导致自聚焦失败。针对此问题，提出了一种基于近似观测和最小熵约束的稀疏自聚焦方法。首先, 为解决测量矩阵规模大、内存占用高的问题, 构建了一种基于近似观测的稀疏自聚焦模型, 在聚焦图像的傅里叶变换域引入误差相位。然后, 在采用最大似然估计器估计误差相位时增加了最小熵约束, 同时采用相位梯度自聚焦法提供误差相位的初始解, 有效降低了迭代次数并使迭代结果更接近全局最优解。机载合成孔径雷达的实测数据成像结果表明, 与常规自聚焦方法相比, 所提方法具有更快的收敛速度和更稳定的自聚焦性能。

关键词: 合成孔径雷达, 稀疏自聚焦, 近似观测, 最小熵约束

Abstract:

When the sampling rate is low and the number of reconstruction scatterers is large, the convergence speed of the two-step optimization based sparse autofocus metgod is slow and it is also easy to fall into the local optimal solution with large reconstruction error, causing the autofocus to fail. Aiming at this problem, a sparse autofocus method based on the approximate observation and the minimum entropy constraint is proposed. First, to solve the problem of large-size measurement matrix and high memory footprint, a sparsity-driven autofocusing model based on approximate observation is constructed, in which the phase error is introduced in the Fourier transform domain of the focused image.Then, the minimum entropy constraint is added into the maximum likelihood estimation of the phase error. Besides, the phase gradient autofocus method is used to provide the initial solution for the phase error, which effectively reduces the number of iterations and make the iterative result close to the global optimal solution. The imaging results of airborne synthetic aperture radar(SAR) data show that the proposed method has faster convergence speed and more stable self focusing performance than the conventional autofocusing method.

Key words: synthetic aperture radar, sparse autofocus, approximate observation, minimum entropy constraint

中图分类号:

TN958

熊旭颖, 李根, 马彦恒. 基于近似观测和最小熵约束的SAR稀疏自聚焦方法[J]. 系统工程与电子技术, 2021, 43(10): 2803-2811.

Xuying XIONG, Gen LI, Yanheng MA. SAR sparse autofocusing method based on approximate observation and minimum entropy constraint[J]. Systems Engineering and Electronics, 2021, 43(10): 2803-2811.

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参考文献 18

1	LI G , MA Y H , DONG J . Total variation regularization-based compressed sensing synthetic aperture radar imaging[J]. Journal of Applied Remote Sensing, 2018, 12 (4): 045017.
2	NI J C , ZHANG Q , LUO Y , et al. Compressed sensing SAR imaging based on centralized sparse representation[J]. IEEE Sensors Journal, 2018, 18 (12): 4920- 4932. doi: 10.1109/JSEN.2018.2831921
3	毕辉, 张冰尘, 洪文, 等. 基于复图像的稀疏SAR成像方法在高分三号数据上的验证[J]. 雷达学报, 2020, 9 (1): 123- 130.
	BI H , ZHANG B C , HONG W , et al. Validation of sparse SAR imaging method based on complex image on gaofen-3 data[J]. Acta radar Sinica, 2020, 9 (1): 123- 130.
4	杨磊, 李埔丞, 李慧娟, 等. 稳健高效通用SAR图像稀疏特征增强算法[J]. 电子与信息学报, 2019, 41 (12): 2826- 2835. doi: 10.11999/JEIT190173
	YANG L , LI P C , LI H J , et al. Robust and efficient general SAR image sparse feature enhancement algorithm[J]. Acta electronica Sinica, 2019, 41 (12): 2826- 2835. doi: 10.11999/JEIT190173
5	ONHON N O , CETIN M . A sparsity-driven approach for joint SAR imaging and phase error correction[J]. IEEE Trans.on Image Processing, 2012, 21 (4): 2075- 2088. doi: 10.1109/TIP.2011.2179056
6	YANG J G , HUANG X T , THOMPSON J , et al. Compressed sensing radar imaging with compensation of observation position error[J]. IEEE Trans.on Geoscience & Remote Sensing, 2014, 52 (8): 4608- 4620.
7	CHEN Y C , LI G , ZHANG Q , et al. Motion compensation for airborne SAR via parametric sparse representation[J]. IEEE Trans.on Geoscience & Remote Sensing, 2016, 55 (1): 551- 562.
8	ENDER J. A brief review of compressive sensing applied to radar[C]//Proc. of the IEEE 14th International Radar Symposium, 2018: 3-16.
9	YANG J G , THOMPSON J , HUANG X T , et al. Segmented reconstruction for compressed sensing SAR imaging[J]. IEEE trans.on Geoscience and Remote Sensing, 2013, 51 (7): 4214- 4225. doi: 10.1109/TGRS.2012.2227060
10	ZHANG B C , HONG W , WU Y R . Sparse microwave imaging: principles and applications[J]. Science China Information Sciences, 2012, 55 (8): 1722- 1754. doi: 10.1007/s11432-012-4633-4
11	FANG J , XU Z B , ZHANG B C , et al. Fast compressed sensing SAR imaging based on approximated observation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 7 (1): 352- 363.
12	BI H , BI G A , ZHANG B C , et al. Complex-image-based sparse SAR imaging and its equivalence[J]. IEEE Trans.on Geoscience and Remote Sensing, 2018, 56 (9): 5006- 5014. doi: 10.1109/TGRS.2018.2803802
13	BI H , BI G A , ZHANG B C , et al. From theory to application: real-time sparse SAR imaging[J]. IEEE Trans.on Geoscience and Remote Sensing, 2020, 58 (4): 2928- 2936. doi: 10.1109/TGRS.2019.2958067
14	LI B , LIU F L , ZHOU C B , et al. Phase error correction for approximated observation-based compressed sensing radar imaging[J]. Sensors, 2017, 17 (3): 613. doi: 10.3390/s17030613
15	PU W , WU J J , WANG X D , et al. Joint sparsity-based imaging and motion error estimation for BFSAR[J]. IEEE Trans.on Geoscience and Remote Sensing, 2019, 57 (3): 1393- 1408. doi: 10.1109/TGRS.2018.2866437
16	WAHL D E , EICHEL P H , GHIGLIA D C , et al. Phase gradient autofocus-a robust tool for high resolution SAR phase correction[J]. IEEE Trans.on Aerospace and Electronic Systems, 1994, 30 (3): 827- 835. doi: 10.1109/7.303752
17	BECK A , TEBOULLE M . A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2 (1): 183- 202. doi: 10.1137/080716542
18	WRIGHT S J , NOWAK R D , FIGUEIREDO M A T . Sparse reconstruction by separable approximation[J]. IEEE Trans.on Signal Processing, 2009, 57 (7): 2479- 2493. doi: 10.1109/TSP.2009.2016892

参数	数值	参数	数值
波长	X波段	斜视角/(°)	0
距离向带宽/MHz	450	平台高度/m	7 500
距离向采样率/MHz	550	中心斜距/km	23.6
脉冲宽度/μs	2.4	速度/(m/s)	154
脉冲重复频率/Hz	533.3	-	-

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基于近似观测和最小熵约束的SAR稀疏自聚焦方法

SAR sparse autofocusing method based on approximate observation and minimum entropy constraint

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