稀疏场景下SAR方位向随机丢失数据的迭代成像算法

doi:10.12305/j.issn.1001-506X.2021.07.03

摘要/Abstract

摘要：

针对合成孔径雷达(synthetic aperture radar, SAR)方位向随机丢失部分数据导致目标模糊和能量分散的问题, 提出基于稀疏优化理论的重建成像方法。该方法主要针对稀疏观测场景的SAR方位向随机缺失数据的回波信号进行成像处理, 利用SAR回波模拟算子, 避免了二维回波信号矩阵变成向量的操作, 从而减小了内存占用和计算量。所提出的基于SAR近似观测模型的迭代优化重建算法能够实现对观测目标幅度的高精度重建。和传统基于匹配滤波的SAR成像算法相比, 提出的算法能够明显地消除SAR方位向随机丢失部分数据引起的目标模糊和目标能量分散。和迭代软阈值收缩算法相比, 提出的算法重建的目标幅度误差更小。理想的点目标回波数据和真实的星载SAR稀疏观测场景的回波数据处理表明了所提算法在减少重建目标误差、提高观测目标区域的目标背景比等方面有较大的优势。

关键词: 合成孔径雷达成像, 随机丢失数据, 近似观测模型, 迭代优化算法

Abstract:

In order to solve the problem of target ambiguity and energy dispersion caused by the random missing data in synthetic aperture radar (SAR) azimuth, a novel reconstruction imaging method based on sparse optimization theory is proposed. This method mainly deals with the echo signal of SAR azimuth random missing data in sparse observation scene. The SAR echo simulation operator is used which can avoid the operation of two-dimensional echo signal matrix into vector and reduces complexity of memory consumption and computational. The iterative optimization reconstruction algorithm based on SAR approximate observation model can realize the high-precision reconstruction of the amplitude of the observed targets. Compared with the traditional SAR imaging algorithm based on matching filter, the proposed algorithm can obviously eliminate the targets ambiguity and targets energy dispersion caused by the random missing data in SAR azimuth. Compared with the iterative shrinkage thresholding algorithm, the proposed algorithm has smaller target amplitude error. The ideal point targets echo data and the real sparse observation scene spaceborne echo data processing show that the proposed algorithm has great advantages in reducing the reconstruction error of targets amplitude and improving the target background ratio in the observed targets area.

Key words: synthetic aperture radar imaging (SAR), random missing data, approximate observation model, iterative optimization algorithm

中图分类号:

TN95

杨卫星, 朱岱寅. 稀疏场景下SAR方位向随机丢失数据的迭代成像算法[J]. 系统工程与电子技术, 2021, 43(7): 1748-1755.

Weixing YANG, Daiyin ZHU. Iterative imaging algorithm for SAR azimuth random missing data with sparse scenes[J]. Systems Engineering and Electronics, 2021, 43(7): 1748-1755.

图/表 15

表1

表2

图1

图2

图3

图4

图5

图6

表3

图7

图8

图9

图10

表4

表5

参考文献 31

1	MOREIRA A , PRATS-IRAOLA P , YOUNIS M , et al. A tutorial on synthetic aperture radar[J]. IEEE Geoence & Remote Sensing Magazine, 2013, 1 (1): 6- 43.
2	洪文, 王彦平, 林赟, 等. 新体制SAR三维成像技术研究进展[J]. 雷达学报, 2018, 7 (6): 633- 654.
	HONG W , WANG Y P , LIN Y , et al. Research progress on three-dimensional SAR imaging techniques[J]. Journal of Radars, 2018, 7 (6): 633- 654.
3	邓云凯, 禹卫东, 张衡, 等. 未来星载SAR技术发展趋势[J]. 雷达学报, 2020, 9 (1): 1- 33.
	DENG Y K , YU W D , ZHANG H , et al. Forthcoming spaceborne SAR development[J]. Journal of Radars, 2020, 9 (1): 1- 33.
4	BRUDER J A, SCHNEIBLE R. Interrupted SAR waveforms for high interrupt ratios[C]//Proc. of the Institution of Engineer and Technology International Conference on Radar Systems, 2007.
5	SALZMAN J , AKAMINE D , LEFEVER R , et al. Interrupted synthetic aperture radar (SAR)[J]. IEEE Aerospace and Electronic Systems Magazine, 2002, 17 (5): 33- 39. doi: 10.1109/62.1001990
6	IVANA S , KARL W C , NOVAK L . Reconstruction of interrupted SAR imagery for persistent surveillance change detection[J]. Algorithms for Synthetic Aperture Radar Imagery, 2012, 8746 (1): 87460.
7	AHMED N, UNDERWOOD C. Monostatic CW-SAR concept for microsatellites[C]//Proc. of the 8th European Conference on Synthetic Aperture Radar, 2010.
8	BROERSEN P, WAELE S, BOS R. Estimation of autoregre-ssive spectra with randomly missing data[C]//Proc. of the 20th IEEE Instrumentation and Measurement Technology Confe-rence, 2003.
9	STOICA P , LARSSON E G , LI A J . Adaptive filter-bank approach to restoration and spectral analysis of gapped data[J]. Astronomical Journal, 2000, 120 (4): 2163- 2173. doi: 10.1086/301572
10	WANG Y W , STOICA P , et al. Nonparametric spectral analysis with missing data via the EM algorithm[J]. Digital Signal Processing, 2005, 15 (2): 191- 206. doi: 10.1016/j.dsp.2004.10.004
11	TARIK Y , LI J , PETRE S , et al. Source localization and sensing: a nonparametric iterative adaptive approach based on weighted least squares[J]. IEEE Trans.on Aerospace and Electronic Systems, 2010, 46 (1): 425- 443. doi: 10.1109/TAES.2010.5417172
12	VU D , XU L , XUE M , et al. Nonparametric missing sample spectral analysis and its applications to interrupted SAR[J]. IEEE Journal of Selected Topics in Signal Processing, 2012, 6 (1): 1- 14. doi: 10.1109/JSTSP.2011.2168192
13	PATEL V M , EASLEY G R , HEALY D M , et al. Compressed synthetic aperture radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4 (2): 244- 254. doi: 10.1109/JSTSP.2009.2039181
14	TELLO A M T , LOPEZ D F , MALLORQUI J J . A novel strategy for radar imaging based on compressive sensing[J]. IEEE Trans.on Geoence and Remote Sensing, 2011, 48, 4285- 4295.
15	BARANIUK R, STEEGHA P. Compressive radar imaging[C]//Proc. of the IEEE Radar Conference, 2007.
16	ZENG J S , FANG J , XU Z B . Sparse SAR imaging based on L_1/2 regularization[J]. Science China Information Sciences, 2012, 55 (8): 1755- 1775. doi: 10.1007/s11432-012-4632-5
17	YANG J G , THOMPSON J , HUANG X T , et al. Segmented reconstruction for compressed sensing SAR imaging[J]. IEEE Trans.on Geoence & Remote Sensing, 2013, 51 (7): 4214- 4225.
18	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 & Remote Sensing, 2013, 7 (1): 352- 363.
19	WRIGHT S J , NOWAK R D , MÁRIO A T F . Sparse reconstruction by separable approximation[J]. IEEE Trans.on Signal Processing, 2009, 57 (7): 2479- 2493. doi: 10.1109/TSP.2009.2016892
20	FANG J , WU Y R , XU Z B , et al. Efficient regularization algorithm with range-azimuth decoupled for SAR imaging[J]. Electronics Letters, 2014, 50 (3): 204- 205. doi: 10.1049/el.2013.1989
21	BI H , ZHANG B C , ZHU X X , et al. Extended chirp scaling-baseband azimuth scaling-based azimuth-range decouple L₁ re-gularization for tops SAR imaging via CAMP[J]. IEEE Trans.on Geoscience & Remote Sensing, 2017, 55 (7): 3748- 3763.
22	BI H , ZHANG B C , ZHU X X , et al. L₁-regularization-based SAR imaging and CFAR detection via complex approximated message passing[J]. IEEE Trans.on Geoscience and Remote Sensing, 2017, 55 (6): 3426- 3440. doi: 10.1109/TGRS.2017.2671519
23	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 Sensin, 2020, 58 (4): 2928- 2936. doi: 10.1109/TGRS.2019.2958067
24	RANEY K , RUNGE H , BAMLER R , et al. Precision SAR processing using chirp scaling[J]. IEEE Trans.on Geoscience and Remote Sensing, 1994, 32 (4): 786- 799. doi: 10.1109/36.298008
25	吴一戎, 洪文, 张冰尘. 稀疏微波成像导论[M]. 北京: 科学出版社, 2018.
	WU Y R , HONG W , ZHANG B C . Introduction to sparse microwave imaging[M]. Beijing: Science Press, 2018.
26	李清泉, 王欢. 基于稀疏表示理论的优化算法综述[J]. 测绘地理信息, 2019, 44 (4): 1- 9.
	LI Q Q , WANG H . Sparse representation-based optimization: a surey[J]. Journal of Geomatics, 2019, 44 (4): 1- 9.
27	CAI J F , OSHER S , SHEN Z W . Linearized bregman iterations for compressed sensing[J]. Mathematics of Computation, 2008, 78 (2): 1515- 1536.
28	CAI J F , OSHER S , SHEN Z . Linearized bregman iterations for frame-based image deblurring[J]. SIAM Journal on Imaging Sciences, 2009, 2 (1): 226- 252. doi: 10.1137/080733371
29	QIAO T , LI W , WU B . A new algorithm based on linearized bregman iteration with generalized inverse for compressed sensing[J]. Circuits Systems & Signal Processing, 2014, 33 (5): 1527- 1539.
30	洪文, 胡东辉, 吴一戎, 等. 合成孔径雷达成像——算法与实现[M]. 北京: 电子工业出版社, 2007.
	HONG W , HU D H , WU Y R , et al. Digital processing of synthetic aperture radar data: algorithms and implementation[M]. Beijing: Electronics industry Press, 2007.
31	SAMADI S , CETIN M , MASNADI-SHIRAZI M A . Sparse representation-based synthetic aperture radar imaging[J]. Institution of Engineer and Technology Radar Sonar & Navigation, 2011, 5 (2): 182- 193.

算子	含义
F_a	方位向傅里叶变换
F_a^-1	方位向逆傅里叶变换
F_r	距离向傅里叶变换
F_r^-1	距离向逆傅里叶变换
Θ_sc	chirp-scaling解耦算子
Θ_rc	距离压缩和一致距离徙动校正
Θ_ac	方位压缩和相位校正

参数	仿真数值
到场景中心斜距/km	1 000
飞行速度/(m/s)	7 000
斜视角/(°)	0
载频/GHz	5
距离向调频率/(MHz/μs)	20
脉冲宽度/μs	50
脉冲重复频率/Hz	4 000
距离向采样率/MHz	25.6

算法	目标
算法	1	2	3	4	5	6	7	8	9
方位向任意缺失25%数据(本文算法)	1.331 8	9.816 9	0.303 7	0.024 4	0.156 7	0.017 1	0.692 4	3.251 5	0.430 9
方位向任意缺失25%数据(IST算法)	97.098 7	84.826 6	71.133 0	80.744 9	69.130 7	61.572 2	83.202 8	70.902 4	61.241 7
方位向任意缺失50%数据(本文算法)	5.141 3	7.010 1	1.693 0	0.226 1	4.209 8	0.099 0	2.834 7	3.491 2	2.221 0
方位向任意缺失50%数据(IST算法)	93.986 3	83.488 6	69.106 7	80.397 0	70.660 7	59.565 1	83.130 5	69.304 0	60.079 3

算法	目标
算法	1	2	3	4	5	6
方位向全孔径数据(Chirp-Scaling算法)	44.126 4	49.521 1	42.320 6	50.0812	40.442 9	40.944 8
方位向任意缺失25%数据(本文算法)	79.505 5	90.705 3	116.451 3	84.030 7	81.914 4	79.018 1
方位向任意缺失25%数据(IST算法)	69.042 8	71.911 2	71.525 1	72.455 3	68.776 9	66.573 8
方位向任意缺失50%数据(本文算法)	92.004 2	101.174 6	93.708 9	92.097 1	90.017 2	85.451 0
方位向任意缺失50%数据(IST算法)	73.110 5	77.309 3	75.190 3	76.626 4	73.537 5	69.357 0

算法	目标
算法	1	2	3	4	5	6
方位向任意缺失25%数据(本文算法)	2.741 2	4.535 9	1.320 5	1.428 3	2.507 7	1.828 7
方位向任意缺失25%数据(IST算法)	10.599 7	19.079 0	11.647 4	17.940 3	12.312 6	12.404 4
方位向任意缺失50%数据(本文算法)	3.980 2	12.078 0	4.705 9	7.123 9	6.164 9	5.010 2
方位向任意缺失50%数据(IST算法)	21.695 3	35.319 6	23.118 7	37.114 8	22.336 1	22.508 0