Direction-of-arrival estimation of incoherently distributed sources using Bayesian compressive sensing

Much attention has recently been paid to incoherently distributed sources (IDSs) in this field of direction-of-arrival (DOA) estimation of distributed sources. This study introduces the sparse reconstruction theory to DOA estimation of IDSs and proposes two DOA estimation methods of IDSs based on different sparse representations. First, the DOA estimation problem of IDSs is translated into a sparse reconstruction problem by building two parametric sparse representation models (on-grid and off-grid), which indicate the fact that the central DOAs of IDSs may be not on the discretised sampling grid. Then, based on the two different models, the corresponding sparse reconstruction problem is solved using the Bayesian compressive sensing method. Accordingly, the central DOA estimation of IDSs is also obtained. Compared with the existing methods, the authors’ approach can provide a better performance under the condition of low signal-to-noise ratio, limited number of snapshots and closely spaced sources. Simulation results demonstrate the effectiveness of the proposed methods.