High-Precision Direction of Arrival Estimation for Closely Spaced Targets Using Binary-Phase Reconfigurable Intelligent Surfaces and Minimum Redundancy Linear Arrays

Raees Danaee, Meysam; Ataei, Ahmad

doi:10.22060/eej.2025.24292.5674

High-Precision Direction of Arrival Estimation for Closely Spaced Targets Using Binary-Phase Reconfigurable Intelligent Surfaces and Minimum Redundancy Linear Arrays

Document Type : Research Article

Authors

Meysam Raees Danaee ¹

Ahmad Ataei ²

¹ Assistant Professor, Faculty of Electrical Engineering, Imam Hossein University, Tehran, Iran

² PhD Candidate of Electrical Engineering, Faculty of Electrical Engineering, Imam Hossein University, Tehran, Iran

10.22060/eej.2025.24292.5674

Abstract

A novel method for enhancing the accuracy of direction of arrival estimation for two closely spaced targets by optimizing the geometric array configuration of a Binary-Phase Reconfigurable Intelligent Surface based on Minimum Redundancy Linear Arrays is proposed. In Binary-Phase Reconfigurable Intelligent Surfaces, the phases of the reflected signals at the Reconfigurable Intelligent Surfaces elements remain unchanged or undergo a 180-degree phase shift, making it significantly more cost-effective in terms of hardware compared to traditional direction of arrival estimation systems. This cost reduction, however, leads to an increase in the correlation of dictionary atoms. To compensate for this drawback, we regularize the optimization problem using atomic norm. Subsequently, the problem is transformed into its dual form to facilitate solving with existing solvers. Simulation results demonstrate that the proposed method can estimate the direction of arrival with higher accuracy for closely spaced targets in the angular domain, compared to existing Reconfigurable Intelligent Surfaces-based array methods, while maintaining the same hardware complexity.

Keywords

DOA estimation

Binary-Phase Reconfigurable Intelligent Surface (Binary-Phase RIS)

Minimum Redundancy Linear Arrays (MRLAs)

Atomic Norm Regularization

Subjects