Empirical Mode Decomposition based denoising for high resolution Direction of Arrival estimation

In this work, Empirical Mode Decomposition (EMD) is applied to the problem of Direction of Arrival (DoA) estimation as a preprocessing method. The preprocessing stage consists of separate denoising the rows of the array data matrix where each row corresponds to the output of a particular array sensor. The chosen denoising algorithm is an iterative interval-thresholding variant of EMD. After the denoising stage, MUSIC is applied to construct the EMD-enhanced spatial spectrum. The proposed EMD-based array denoising scheme is based on the principles of wavelet-thresholding, thus it is comparable to wavelet-based denoising of array matrix. The results show that, especially in low-SNR scenarios, the estimation performance of MUSIC is significantly enhanced when denoising is applied to array data matrix prior to DoA estimation stage.