An improved Richardson-Lucy algorithm for radar angular super-resolution

The traditional Richardson-Lucy (R-L) algorithm has a strong ability to realize super-resolution. However, it always suffers from noise amplification. In this paper, an improved R-L algorithm is proposed to solve the real-beam scanning radar angular super-resolution problem, which relies on both the traditional R-L deconvolution algorithm and the regularization term. We first describe the angular super-resolution problem as a deconvolution task and formulate our improved R-L decon-volution algorithm from a Bayesian framework. We then solve the angular super-resolution problem in Bayesian framework using the improved R-L algorithm, which lead to the fixed-point iterative method. Experimental results with synthetic data illustrate that the performance of proposed algorithm is better than conventional R-L algorithm.