MCA Learning Algorithm for Incident Signals Estimation: A Review

TL;DR

This paper reviews and presents a Minor Component Analysis (MCA) learning algorithm for estimating incident signals, focusing on fast convergence and application in direction of arrival (DOA) estimation with simulation validation.

Contribution

It introduces a new MCA learning algorithm with a specific learning rate parameter to improve convergence speed in incident signal estimation.

Findings

Algorithm achieves fast convergence in simulations.

Effective in estimating the direction of arrival (DOA).

Theoretical analysis supports simulation results.

Abstract

Recently there has been many works on adaptive subspace filtering in the signal processing literature. Most of them are concerned with tracking the signal subspace spanned by the eigenvectors corresponding to the eigenvalues of the covariance matrix of the signal plus noise data. Minor Component Analysis (MCA) is important tool and has a wide application in telecommunications, antenna array processing, statistical parametric estimation, etc. As an important feature extraction technique, MCA is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will present a MCA learning algorithm to extract minor component from input signals, and 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.