A Novel Extended Cyclic MUSIC algorithm using Wavelet Decomposition Technique

The study and analysis of direction of arrival(DOA) estimation has been a very challenging aspect in wireless communication systems, radar and sonar in the presence of a fading channel. Extended cyclic MUSIC subspace based method is reviewed for the DOA when noise is present. An extension of the Cyclic MUSIC algorithm is the Extended mode of the cyclic MUSIC which uses an extended version of the data array vector. Based upon the spectral correlation of the inbound signals these algorithms can estimate the signals’ direction of arrival. The spatially correlated noises, present degrade the performance of the Cyclic MUSIC algorithms and also fail to make an estimate of the signals' direction of arrival when they are closely spaced. The paper proposes an improved DOA estimation technique of Wavelet Decomposition when spatially correlated noises are present. The signals present at the receiver side are denoised and the required estimates of the transmitted signals are obtained by using the Extended Cyclic MUSIC algorithm to the denoised data. Simulations prove that the output SNR has been enhanced considerably