Passive synthetic aperture for direction-of-arrival estimation using sparse Bayesian learning

Abstract

Passive synthetic aperture (PSA) extension for a moving array has the ability to enhance the accuracy of direction-of-arrival (DOA) estimation by constructing a larger virtual aperture. The array element overlap in array continuous measurements is required for the traditional extended towed array measurement (ETAM) methods. Otherwise, the phase factor estimation is biased, and the aperture extension fails when multiple sources exist. To solve this problem, passive aperture extension with sparse Bayesian learning (SBL) is proposed. In this method, SBL is used to simultaneously estimate the phase correction factors of different targets, followed by phase compensation applied to the extended aperture manifold vectors for DOA estimation. Simulation and experimental data results demonstrate that this proposed method successfully extends the aperture and provides higher azimuth resolution and accuracy compared to conventional beamforming (CBF) and SBL without extension. Compared with the traditional ETAM methods, the proposed method still performs well even when the array elements are not overlapped during the motion.