Direction of Arrival Estimation of Wideband Sources Using Sparse Linear Arrays

In this paper, we study the problem of wideband direction arrival (DoA) estimation with sparse linear arrays (SLAs), where a number uncorrelated signals impinge on an SLA and data is collected from multiple frequency bins. To boost performance perform underdetermined DoA estimation, difference co-array response matrices for all bins are constructed first. Then, to merge different bins, resort Jacobi-Anger approximation transform into single virtual uniform array (ULA) matrix. The major advantage approach that transformation signal independent. For special case sources share identical distribution power spectrum, develop two super-resolution off-the-grid approaches based atomic norm minimization (ANM), one without prior knowledge spectrum. Our solution able resolve more than antennas but also degrees freedom (DoF) SLA. general each source has arbitrary propose multi-task ANM method exploit joint sparsity Simulation results show our proposed methods present clear over existing methods, achieve accuracy close associated Cramér-Rao bounds (CRBs).