基于压缩感知的低复杂度超分辨角度估计方法

In aerial target angle estimation, the resolution is constrained by the aperture length. Increasing the number of array elements to improve the resolution will increase the system lost. To reduce the number of elements in the limited array size, a novel algorithm of super-resolution angle estimation is addressed based on compressive sensing (CS) theory. The array received signal model is established and the redundant dictionary is formed. By exploiting the sparse prior information of the observation area, the target angle estimation problem is converted into the optimization problem. The direction of arrival of targets can be estimated with accuracy via an iterative optimization algorithm. In the proposed algorithm, the regularization coefficient is derived by Bayesian CS theory to ensure the noise robustness of the algorithm. Besides, the efficiency of the proposed algorithm is improved by using the conjugate gradient algorithm and Hadamard product. The effectiveness of the proposed algorithm is verified by simulation and measured data.