Pattern recognition (psychology)

Radar imaging

Algorithm

Inverse synthetic aperture radar

Compressed sensing

Minification

Artificial intelligence

Regularization (linguistics)

Programming language

Computer science

Synthetic aperture radar

Radar

Telecommunications

IEEE Sensors Journal

Sparse aperture radar imaging is generally achieved by methods of compressive sensing (CS), or, sparse signal recovery (SSR). However, most of the traditional SSR methods cannot produce focused image stably, which limits their applications. l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization and alternating direction method of multipliers(ADMM) are generally applied to the SSR problem, but its performance is sensitive to the selection of model parameters. This paper proposes a complex-valued ADMM-Net(CV-ADMMN) method to improve the stability of ADMM, and utilize it to achieve sparse aperture ISAR imaging and autofocusing. Firstly, the iterative procedure of ADMM is unrolled to be a deep network structure. Then, the parameters of the model are learned from a training dataset by utilizing an l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularized loss function. Finally, an autofocusing module based on entropy-minimization is plugged into the trained model to compensate the phase error. Experimental results based on both simulated and measured data validate the superiority of the proposed method over ADMM.

Deep Learning Approach for Sparse Aperture ISAR Imaging and Autofocusing Based on Complex-Valued ADMM-Net