Direct localization of wideband sources using distributed arrays: A subspace focusing and dimension reduction approach

Abstract

To enhance the localization performance of wideband sources with distributed arrays, this paper proposes a subspace focusing and dimension reduction approach. Firstly, the received sensor data are subjected to data segmentation and frequency domain transformation, and the processed data are fused into a dataset. Then, utilizing the subspace focusing algorithm, a focusing matrix is constructed to concentrate the data from each frequency point onto the reference frequency point, which can solve the problem of rank deficient covariance matrix caused by signal coherence. Thereafter, the cost function is established by utilizing the subspace orthogonality, and dimensionality reduction is employed to avoid the problem of unknown attenuation coefficients. Finally, the estimated position of the wideband sources are obtained by searching for the spectral peaks of the cost function. Simulation results show the effectiveness of the proposed method, whose positioning performance is improved compared to other algorithms.