Direction of Arrival Identification Using MUSIC Method and NLMS Beamforming
Raungrong Suleesathira · 2020
This paper provides the capability of the direction of arrival (DOA) identification to determine which the estimated DOA belongs to the desired signal and to undesired signals. One of the well known subspace-based methods for finding directions is MUSIC (MUltiple Signal Classification). The separation of signal and noise subspaces is the crucial step to give the precise estimation. The skewness coefficient is proposed to reinforce the conventional MUSIC method for the subspace division without knowing the number of source signals. The normalized least mean square (NLMS) beamforming is used to compute the weight vector so that it directs the mainbeam towards the desired user. The angle of the mainbeam is identified to be the DOA of the desired signal which makes the rest estimated DOAs belong to interference signals. The application of the DOA identification is shown to be advantageous to the null broadening beamforming. The simulation results confirm the effectiveness of the proposed method in the case of limited snapshots.