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

Abstract

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

CLC Number:

TN95

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.

Figures/Tables 15

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References 31

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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

Iterative imaging algorithm for SAR azimuth random missing data with sparse scenes

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