Direction Finding in Partly Calibrated Arrays Using Sparse Bayesian Learning

Abstract

Direction finding in partly calibrated arrays, a distributed array with errors between subarrays, receives wide studies. Recently, sparse recovery is used to exploit the blockand rank- sparsity of the signals to self-calibrate the errors and recover the directions, which achieves good performance. Compared with traditional methods based on subspace separation, sparse recovery methods are less sensitive to few snapshots and correlated sources. However, existing sparse recovery methods solve a complex semi-definite programming (SDP) problem, which suffers from high time and space complexity. To this end, we consider to introduce sparse Bayesian learning (SBL) to partly calibrated arrays instead. In a SBL framework, we formulate a sparse recovery problem with self-calibration on errors, and derive the closed-form iterations to solve the problem. Simulations show the feasibility of our proposed method and less time complexity than existing sparse recovery methods.