Joint DOA and Frequency Estimation With Spatial and Temporal Sparse Sampling Based on 2-D Covariance Matrix Expansion

This article addresses the challenge of simultaneously estimating the direction of arrival (DOA) and signal carrier frequency when sampling is sparse in both the spatial and temporal domains. To capture the arbitrary distribution of source spectra, a linear array receiver based on multicoset sampling is employed. Utilizing subspace decomposition, a 2-D pseudospectrum-based joint DOA and carrier frequency estimation (TDPS-JDFE) algorithm is proposed. In addition, for sparse delay patterns and sparse arrays, a 2-D covariance matrix expansion (CME) version of the proposed TDPS-JDFE method is developed, which greatly increases the degrees of freedom of the array by expanding the dimensions of the covariance matrix both spatially and temporally. The resulting TDPS-JDFE-CME method offers significantly increased potential for estimating the maximum number of sources. The related characteristics of the proposed methods are analyzed, and the performance of the proposed methods is verified via simulation experiments.