Multiple Near-field Sources Localization by Optimal Beamformer Using Minimum Norm Method

Direction of arrival (DOA) and range estimation performance by a near-field narrowband optimal beamformer is analyzed in this paper using Minimum Norm Method (MNM). MNM is a source localization technique search for the peaks based on eigen-decomposition of the correlation matrix and it has the ability to locate multiple sources with less complexity and error. Localization accuracy and localization resolution in terms of both angles and distance are analyzed in this article. The simulation results show that the proposed MNM based beamformer can easily identifies near-field multiple sources with high accuracy and resolution in the noisy environment. Root mean square error (RMSE) to localize the sources is also measured and compared with Sparse Signal Reconstruction (SSR) based near-field source localization method. The proposed MNM based beamformer provides lower RMSE than SSR based method and shows zero RMSE at certain Signal to Noise Ratio (SNR) level and snapshot number. Array localization performance with respect to the number of sensors, number of snapshots and SNR level are also showed in this paper.