Fast Adaptive Sparse Iterative Reweighted Super-Resolution Method for Forward-Looking Radar Imaging

Recently, a sparse super-resolution method based on $L_{1}$ iterative reweighted norm (IRN) has been proposed to improve the azimuth resolution of forward-looking radar. However, this method suffers from poor adaptability and high computational complexity due to its noise-sensitive user-parameter and the necessity for high-dimensional matrix inversion. To this end, a fast adaptive $L_{1}$-IRN sparse super-resolution method is derived in this article, allowing for the user-parameter-free and efficient sparse imaging of forward-looking radar. First, we establish the super-resolution model of forward-looking radar and analyze the user parameter selection problem in the conventional $L_{1}$-IRN method. Second, based on Bayesian theory, adaptive iterative weights of different azimuths are derived by transforming the sparse estimation problem into a maximum posterior (MAP) estimation problem. Finally, by using QR decomposition and Sherman–Morrison formula, the dimensionality of the echo and antenna pattern involved in the iteration is reduced to further diminish the computational complexity. Compared to the existing $L_{1}$-IRN method, the proposed method eliminates the need for any user parameters, and the computational complexity has been reduced from ${O}({JN}^{3})$ to ${O}({JN}^{2}{a})$. Simulation and measured data demonstrate the superiority of the proposed method.