Mixed near-field and far-field source localization utilizing symmetric nested array

Abstract

Existing mixed near-field and far-field source localization algorithms rely on uniform linear array (ULA), and the maximum number of sources that they can detect is less than the sensor number. As a comparison, recently proposed nested array can provide higher number of consecutive lags with the same sensor number, enabling us to extend it to mixed source localization scenario. Instead of utilizing generalized nested array, we design a symmetric nested array (SNA) for mixed source localization. By constructing a fourth-order cumulant matrix that only related to DOA information, the newly designed symmetric nested array exhibits at least of 4N+4MN−2M−3 consecutive lags with only 2M+2N−1 sensors. A compressive sensing (CS) based approach is exploited to achieve DOA estimation of all sources by fully using these consecutive lags. Based on the estimated DOAs, the range of near-field sources is obtained by 1-D spectral search, and the near-field and far-field sources are classified successively in range domain. By conducting numerical simulations, we show the superiority of the proposed algorithm in terms of estimation accuracy, number of required array sensors, and resolution ability compared with previous algorithms.