An iterative shrinkage threshold method for radar angular super-resolution. (23rd April 2019)

• Record Type: Journal Article
• Authors: Zhang, Xin; Liu, Xiaoming; Liu, Chang; Na, Zhenyu
• Is Part Of: International journal of embedded systems. Volume 11:Number 3(2019)
• Page Start: 285
• Page End: 294
• Publication Date: 2019-04-23
• Subjects: radar -- super-resolution -- constrained optimisation -- iterative shrinkage threshold -- IST; Embedded computer systems -- Periodicals; 004.16
• Languages: English
• ISSNs: 1741-1068

Abstract

This paper proposes a fast iterative shrinkage threshold (FIST) method for improving radar angular resolution. Based on radar signal processing theory, the implementation of angular super-resolution is equivalent to restoring the radar's target angular information without changing radar's work system. In this method we first establish a convex quadratic programming model by orthogonalising the antenna pattern matrix, which transforms the radar angular super-resolution problem into a constrained optimisation problem. Consequently, the restored angular information can be regarded as the optimal solution of a convex quadratic programming model. Then, the IST algorithm is employed by modifying the residual at each iteration to find this optimal solution of the model. The advantage of this method is to overcome the shortcoming of ringing effect at a low signal to noise ratio (SNR) situation, and the ill-posed problem existing in those classical super-resolution methods is addressed effectively. Simulations further confirm our theoretical discussion, and manifest that a desirable resolution performance is gained and comparisons of signal to restoration error ratio (SRER) provide an amazing result that our method is superior to other methods in terms of efficiency while SNR is less than 20 dB.