Abstract
Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3). In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O(N3) to O(N2), benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform.
Highlights
Airborne radar plays an important role in many fields for its all-day and all-weather imaging ability [1,2]
The complexity is compared with some traditional super-resolution methods, including truncated singular value decomposition (TSVD) [8], Iterative adaptive approach (IAA) [9], sparse [25] and Total variation (TV) methods [15]
The logarithmic computational complexity curves demonstrate the great advantage of the proposed GS representation based fast TV (GSFTV) method in computational complexity
Summary
Qiping Zhang † , Yin Zhang *,† , Yongchao Zhang † , Yulin Huang † and Jianyu Yang †. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O( N 3 ) to O( N 2 ), benefiting efficiency improvement for airborne radar imaging. The simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour.
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