Multiple Signal Classification Algorithm for Spatial Spectrum Estimation Principles and Algorithms

This article explores the implementation of the Multiple Signal Classification (MUSIC) algorithm based on the methodology described in "Principles and Algorithms of Spatial Spectrum Estimation." The core concept utilizes multiple sensors or antennas to capture signals and combines them to enhance signal quality. The implementation details cover several critical aspects including optimal sensor array positioning, determination of appropriate weighting coefficients, and noise reduction techniques in signal processing. The algorithm implementation involves computing the covariance matrix from received signals, performing eigenvalue decomposition to separate signal and noise subspaces, and constructing the spatial spectrum using the MUSIC pseudospectrum formula. We provide comprehensive MATLAB source code that demonstrates key functions such as array geometry setup, covariance matrix calculation, eigenvalue decomposition using MATLAB's eig() function, and spectrum peak detection. The code includes practical implementation of steering vector computation and spatial spectrum visualization using MATLAB's plotting capabilities. Through this tutorial, readers will gain deeper understanding of the MUSIC algorithm's implementation workflow and can adapt the provided code for their own projects involving direction-of-arrival estimation and spatial signal processing applications.