Classification and localization of mixed near-field and far-field sources using mixed-order statistics

In this paper, a novel algorithm based on mixed-order statistics is proposed for mixed near-field and far-field source localization. Firstly, the direction-of-arrivals (DOAs) of far-field signals are estimated using the conventional MUSIC method based on second-order statistics. Then, a special fourth-order cumulant matrix of the array output is constructed, which is only related to DOA parameters of mixed sources. After estimating the kurtosis of far-field signals, the related far-field components can be removed from the constructed cumulant matrix and the near-field components can be derived. With the near-field data in the cumulant domain, the DOA estimations of near-field sources can be performed using high-order MUSIC spectrum. Finally, with the near-field DOA estimates, the range parameters of near-field sources can be obtained via one-dimensional search. The proposed algorithm involves neither two-dimensional search nor additional parameter pairing processing. Moreover, it can achieve a more reasonable classification of the source types. Simulations results demonstrate the advantages of the proposed algorithm in comparison to the existing methods.