Localization for Mixed Near-Field and Far-Field Sources under Impulsive Noise

In order to solve the problem that the performance of traditional localization methods for mixed near-field sources (NFSs) and far-field sources (FFSs) degrades under impulsive noise, a robust and novel localization method is proposed. After eliminating the impacts of impulsive noise by the weighted outlier filter, the direction of arrivals (DOAs) of FFSs can be estimated by multiple signal classification (MUSIC) spectral peaks search. Based on the DOAs information of FFSs, the separation of mixed sources can be performed. Finally, the estimation of localizing parameters of NFSs can avoid two-dimension spectral peaks search by decomposing steering vectors. The Cramer-Rao bounds (CRB) for the unbiased estimations of DOA and range under impulsive noise have been drawn. Simulation experiments verify that the proposed method has advantages in probability of successful estimation (PSE) and root mean square error (RMSE) compared with existing localization methods. It can be concluded that the proposed method is effective and reliable in the environment with low generalized signal to noise ratio (GSNR), few snapshots, and strong impulse.