Abstract
Many signal subspace-based approaches have already been proposed for determining the fixed Direction of Arrival (DOA) of plane waves impinging on an array of sensors. Two procedures for DOA estimation based neural network are presented. Firstly, Principal Component Analysis (PCA) is employed to extract the maximum eigenvalue and eigenvector from signal subspace to estimate DOA. Secondly, Minor component analysis (MCA) is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will modify a MCA learning algorithm to enhance the Convergence, where a Convergence is essential for MCA algorithm towards practical applications. The learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Simulation results will be furnished to illustrate the theoretical results achieved.
Highlights
Neural networks have seen an explosion of interest over the last few years and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology, physics and biology
⁄, Programs were written for Direction of Arrival (DOA) estimation in Matlab
2) Effect of added white noise vector Figures (8,9)show the estimated DOA of two sources for incoming signals in Principal Component Analysis (PCA) and modified Minor Component Analysis (MCA), respectively, in order to compare a modified MCA performance with PCA when the input vector is affected by white noise vector
Summary
Neural networks have seen an explosion of interest over the last few years and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology, physics and biology. From a statistical perspective neural networks are interesting because of their potential use in prediction and classification problems [1,2,3]. A maximum likelihood (ML) DOA estimator is derived and sub subsequently shown to be a special case of DOA estimation by means of a search for the direction of maximum steered response power (SRP). The beampattern associated with the ML estimator is shown to be identical to that used by the minimum power distortion with less response beamformer for the purpose of signal enhancement
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