146 / 2019-12-15 09:02:00

2D DOA estimation using sparse Bayesian learning from experimental sparse array data

IEEE SAM 2020: Special Sessions > Robust Beamforming based on Convex/Nonconvex Optimization

For direction finding applications, sparse arrays are specifically designed to resolve more sources than the number of sensors while providing higher resolution than a uniform array with same number of sensors. This has been verified in simulations for one dimensional (1D) and two dimensional (2D) direction-of-arrival (DOA) estimation and in experimental data for 1D DOA estimation. We provide experimental validation of 2D DOA estimation using sparse arrays. The data is collected in an anechoic chamber with a rectangular array. Both co-prime and nested arrays are obtained by sampling this rectangular array. The directions are estimated using sparse Bayesian learning (SBL) which is a compressive sensing algorithm for estimating sparse vectors and their support. SBL is an iterative parameter estimation method and can process multiple snapshots as well as multiple frequency data within its Bayesian framework. The SBL method is compared with conventional beamforming and MUSIC for 2D DOA estimation of experimental data.