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

Abstract

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

CLC Number:

TN958

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.

Figures/Tables 7

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References 18

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

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