Two-Stage Matrix Differencing Algorithm for Mixed Far-Field and Near-Field Sources Classification and Localization

摘要

A novel classification and localization algorithm is proposed for scenarios where both far-field and near-field sources may exist simultaneously. By exploiting the property of the Toeplitz structure associated with the far-field covariance matrix, the covariance differencing technique is first carried out to eliminate the far-field components. That is, the pure near-field components can be obtained. Based on a symmetric uniform linear array, an ESPRIT-like solution can be implemented, and the direction-of-arrival (DOA) and range estimations for the near-field sources are performed. After estimating the powers of the near-field signals, the related near-field components can be eliminated from the signal subspace, and the DOAs for the far-field sources are determined via the MUSIC spectral search. The resultant algorithm can provide the improved estimation accuracy, and it achieves a more reasonable classification of the signals types. Computer simulations are carried out to demonstrate the performance of the proposed method.