Group Sparsity Based Localization for Far-Field and Near-Field Sources Based on Distributed Sensor Array Networks

A distributed sensor array network is studied, where sub-arrays are placed on those distributed observation platforms. In this model, bearing-only source localization is characterized in terms of direction of arrival (DOA) if the sources are far from the entire network, while their locations in the predefined Cartesian coordinate system can be obtained for the near-field case. For wideband signals, the focusing algorithm is applied at each sub-array to form an equivalent single frequency signal model. Then, a compressive sensing (CS) based DOA estimation method employing the group sparsity concept is proposed for far-field sources with the information acquired by all the platforms processed as a whole. This concept is further extended to near field, and a group sparsity based method to localize the near-field sources is derived. The proposed solutions are applicable for both uncorrelated and coherent signals, and the corresponding Cramér-Rao Bounds (CRBs) are derived. Compared with the maximum likelihood estimator (MLE) of forming the final result through a fusion process, where separately estimated unreliable bearing result at even one observation platform would spoil the overall performance, improved performance is achieved by both proposed methods. It is noted that only the covariance matrix in lieu of data samples at each platform is required for centralized processing, and therefore the increase of the data exchange workload among platforms is rather limited.