Angular Super-Resolution Method Of Real Aperture Radar Under Model Mismatch Condition

Most of the existing angular super-resolution techniques are based on the convolution model of the target scattering coefficient and the antenna pattern, and this convolution relationship provides the potential for improving the angular resolution. However, due to the non-ideal working environment in practical applications, the antenna pattern generates phase and amplitude errors, resulting in model mismatch. Model mismatch produces errors during super-resolution processing, degrading the final imaging quality. In order to address the issue, an angular super-resolution method of real aperture radar under model mismatch condition is presented in this paper. First, we introduce an error matrix on the original convolutional model to consider model mismatch errors. Secondly, the target sparse prior is exploited to construct the objective function under the model mismatch condition. Finally, the alternating direction method of multipliers (ADMM) solver is utilized to solve the objective function, obtaining the final imaging result. Simulations verify the effectiveness of the proposed method.