Joint Squared-Sine Function and ANM-Based DOA Estimation With RIS

This letter considered the passive sensing problem and proposed a new direction-of-arrival (DOA) estimation algorithm with joint squared-sine function (SS) and atomic norm minimization (ANM), called SS-ANM. For a reconfigurable intelligent surface (RIS) assisted passive sensing system, the SS and atomic norm (AN) are utilized to construct the optimization problem of DOA estimation, where SS is used as an error cost function and AN is to model the sparsity constraint. This SS-ANM algorithm can achieve accurate estimation when the system noise is impulsive noises, and can effectively suppress interference from wireless access point (AP). Simulation results verify the superiority of the proposed SS-ANM over existing DOA estimation methods.