Robust RIS-Based DOA Estimation With Mixed Constraints

This letter presents a Direction-of-arrival (DOA) estimation algorithm in passive sensing systems with Reconfigurable Intelligent Surface (RIS). In order to improve the estimation accuracy in non-Gaussian noise and access point (AP) interference environments, a joint logarithmic function and atomic norm (LFAN) constrained DOA estimation method is proposed, analyzed, and discussed in detail. The proposed LFAN effectively mitigates impulsive noise and AP interference to achieve precise estimation through a minimization problem using logarithmic function and atomic norm constraints. Simulation results show that the proposed LFAN outperforms existing algorithms in terms of estimation performance.