Mixed Far-Field and Near-Field Source Localization Algorithm via Sparse Subarrays

• Main Authors: Jiaqi Song, Haihong Tao, Jian Xie, Chenwei Sun
• Format: Article
• Language: English
• Published: Hindawi Limited 2018-01-01
• Series: International Journal of Antennas and Propagation
• Online Access: http://dx.doi.org/10.1155/2018/3237167

Based on a dual-size shift invariance sparse linear array, this paper presents a novel algorithm for the localization of mixed far-field and near-field sources. First, by constructing a cumulant matrix with only direction-of-arrival (DOA) information, the proposed algorithm decouples the DOA estimation from the range estimation. The cumulant-domain quarter-wavelength invariance yields unambiguous estimates of DOAs, which are then used as coarse references to disambiguate the phase ambiguities in fine estimates induced from the larger spatial invariance. Then, based on the estimated DOAs, another cumulant matrix is derived and decoupled to generate unambiguous and cyclically ambiguous estimates of range parameter. According to the coarse range estimation, the types of sources can be identified and the unambiguous fine range estimates of NF sources are obtained after disambiguation. Compared with some existing algorithms, the proposed algorithm enjoys extended array aperture and higher estimation accuracy. Simulation results are given to validate the performance of the proposed algorithm.