DOA Estimation using MUSIC and Smooth-MUSIC Algorithms in Uniform Circular Array Pattern Space

This implementation utilizes the MUSIC (Multiple Signal Classification) algorithm and smoothed MUSIC estimation method for Direction of Arrival (DOA) estimation in uniform circular array pattern space. The MUSIC algorithm operates by performing eigendecomposition on the covariance matrix of received signals, separating the signal and noise subspaces to achieve high-resolution source separation. Key functions typically include covariance matrix calculation using x'*x/rx_samples, eigendecomposition via [V,D] = eig(Rxx), and peak searching through spatial spectrum computation. The smoothed MUSIC enhancement applies forward/backward averaging to the covariance matrix, effectively reducing noise interference and improving estimation accuracy. This method is particularly valuable for handling coherent signals and multipath scenarios. Implementation typically involves array manifold vector calculation for circular geometry using Bessel functions or phase mode excitation. These high-resolution techniques find extensive applications in audio signal processing, radar systems, wireless communications, and array signal processing where precise angle detection is critical. The code structure generally follows: data preprocessing, covariance matrix estimation, subspace decomposition, spectrum calculation, and peak detection for DOA extraction.