volume | PIER Journals

Abstract

Sparse signal recovery algorithms can be used to improve radar imaging quality by using the sparse property of strong scatterers. Traditional sparse inverse synthetic aperture radar (ISAR) imaging algorithms mainly consider the recovery of sparse scatterers. However, the scatterers of an ISAR target usually exhibit block or group sparse structure. By utilizing the inherent block sparse structure of ISAR target images, an iterative reweighted l_p(0 < p ≤ 1) block sparse signal recovery algorithm is proposed to enhance imaging quality in this paper. Firstly, an ISAR imaging signal model is established with the aid of sparse basis, and the imaging is mathematically converted into block reweighted cost function optimization problem. Then, an iterative algorithm is used to solve the reweighted function minimization problem. In each iteration, the weights are updated based on the closed form solution of the previous iteration. The proposed method is effective to exploit the underlying block sparse structures which does not need the prior knowledge of the number of the blocks. Real data ISAR imaging results are provided to verify that the proposed algorithm in this paper can achieve better images than the images obtained by several popular sparse signal recovery algorithms.

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Dr. Junjie Feng

Dr. Gong Zhang