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Cheng Ping and Zhao Jiaqun

Abstract

To solve the off grid problem in compressed sensing (CS) based inverse synthetic aperture radar (ISAR) imaging, a fast and accurate algorithm has been proposed in the paper. By jointly estimating the off grid error and the sparse solution, off grid ISAR imaging is transformed into a joint optimization problem. Interestingly, it can be solved efficiently through two least squares problems based on first order Taylor approximation. When applied to complex sinusoids and quasi real ISAR data, the proposed algorithm has got better results than the conventional algorithm. Therefore, it is a promising off grid CS based ISAR imaging algorithm.

Open access

Cheng Ping, Shi Liu and Zhao Jiaqun

Abstract

To solve off-grid problem in compressed sensing, a new reconstruction algorithm for complex sinusoids is proposed. The compressed sensing reconstruction problem is transformed into a joint optimized problem. Based on coordinate descent approach and linear estimator, a new iteration algorithm is proposed. The results of experiments verify the effectiveness of the proposed method.

Open access

Cheng Ping and Zhao Jiaqun

Abstract

To improve the performance of inverse synthetic aperture radar (ISAR) imaging based on compressed sensing (CS), a new algorithm based on log-sum minimization is proposed. A new interpretation of the algorithm is also provided. Compared with the conventional algorithm, the new algorithm can recover signals based on fewer measurements, in looser sparsity condition, with smaller recovery error, and it has obtained better sinusoidal signal spectrum and imaging result for real ISAR data. Therefore, the proposed algorithm is a promising imaging algorithm in CS ISAR.