A Second-Order Statistics-Based Mixed Sources Localization Method With Symmetric Sparse Arrays
Xiaohuan Wu, Jun Yan
Abstract
This letter proposes a localization method for mixed far-field (FF) and near-field (NF) sources based on second-order statistics (SOS) with generalized symmetric sparse linear arrays. First, the DOAs of the FF sources are estimated by using MUSIC method. Then, we use the oblique projection technique to isolate the NF sources from the FF ones and the atomic norm minimization is employed to retrieve the DOAs of the NF sources. Finally, the range information of the NF sources are determined by one-dimensional searching. To against the effect of finite measurements, an iterative procedure is employed to alternatively updating the DOA and range information of NF sources and the oblique projector. Closed-form expression of Crammer-Rao lower bound (CRLB) is derived from the coarray perspective for the sparse arrays. Simulations are carried out to demonstrate the effectiveness of our proposed method.