Alternating projections gridless covariance-based estimation for DOA

Abstract

We present a gridless sparse iterative covariance-based estimation method based on alternating projections for direction-of-arrival (DOA) estimation. The gridless DOA estimation is formulated in the reconstruction of Toeplitz-structured low rank matrix, and is solved efficiently with alternating projections. The method improves resolution by achieving sparsity, deals with single-snapshot data and coherent arrivals, and, with co-prime arrays, estimates more DOAs than the number of sensors. We evaluate the proposed method using simulation results focusing on co-prime arrays.

5 pages, accepted by (ICASSP 2021) 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing

References in corpus (4)

• Gridless DOA Estimation and Root-MUSIC for Non-Uniform Arrays
• Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution
• Non-convex Robust PCA
• Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems

Cited by in corpus (1)

• Gridless DOA Estimation and Root-MUSIC for Non-Uniform Arrays