Abstract
Two-dimensional Underdetermined DOA Estimation of Quasi-stationary Signals via Sparse Bayesian Learning
Highlights
Direction of arrival (DOA) estimation is an important problem in array signal processing, which is widely used in radar, sonar, wireless communication and seismic sensing
This paper studies the 2-D DOA estimation of Quasi-stationary signals (QSS) in the context of sparse representation (SR) framework
The Khatri-Rao transform is applied to the uniform circular array (UCA) data model, which makes that the virtual array aperture of UCA is extended, so that the proposed method has the ability to estimate more signals than number of sensors
Summary
Direction of arrival (DOA) estimation is an important problem in array signal processing, which is widely used in radar, sonar, wireless communication and seismic sensing. Though [8] has proposed DOA estimation of QSS based on the UCA, it assumes that each signal is located at a fixed and known elevation angle. It is the one-dimensional (1-D) DOA estimation in essence. Subspace-based methods proposed in [6] can be directly utilized to estimate the 2-D DOAs of QSS, its estimation performance may deteriorate significantly in low SNR or small snapshots. IM denotes the M M identity matrix and diag( ) is a diagonal matrix composed of the elements of a column vector
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