User-parameter-free adaptive sparse super-resolution method for scanning radar

Abstract

The application discloses a user parameter-free adaptive sparse super-resolution method suitable for scanning radars, and comprises the following steps: step one, constructing a scanning radar signal model in a distance unit; step two, establishing a sparse L1 constraint optimization problem; step three, constructing a sparse Bayesian maximum posterior optimization objective function; step four, calculating the maximum posterior objective function; step five, simplifying the objective function to obtain a weighted parameter iterative equation; step six, outputting a radar noise power iterative estimation value; and step seven, outputting an adaptive sparse super-resolution result. After the iterative equations of various variables are obtained in steps four, five and six, iterative calculation is repeated until the final result converges, and the adaptive sparse super-resolution result is obtained. The application converts the sparse super-resolution problem into a Bayesian maximum posterior estimation problem, thereby adaptively iterating the weight, realizing adaptive sparse super-resolution imaging of the scanning radar, and solving the problems of noise sensitivity of the user parameter and poor noise adaptability.