Explore MATLAB source code curated for "空间谱估计" with clean implementations, documentation, and examples.
This tested and fully functional DOA MUSIC algorithm implementation corresponds to the simulation program on page 110 of Wang Yongliang's "Spatial Spectrum Estimation Theory and Algorithms". The code features proper array signal processing implementation, including covariance matrix computation, eigenvalue decomposition, and pseudospectrum calculation using the MUSIC algorithm's core principles.
Array Signal Processing: Fundamental Algorithms for Spatial Spectrum Estimation Principles - Music, Esprit, and Mp
Comparative analysis of Delay-and-Sum and Capon methods for spatial spectrum estimation in adaptive signal processing, including algorithm implementations and application scenarios
MATLAB implementation of subspace fitting algorithms from "Principles and Algorithms of Spatial Spectrum Estimation"
Implementation of signal source quantity estimation through Gerschgorin Circle Theorem methodology. This approach employs spatial spectrum estimation theory to simulate transmitted signals received and sampled by antenna arrays. The Gerschgorin circle algorithm processes sampled sequences to estimate signal quantity under both white noise and colored noise conditions, with potential MATLAB implementations involving covariance matrix decomposition and eigenvalue analysis.
The fundamental Capon beamforming algorithm, also known as MVDR beamforming, which can also be applied to spatial spectrum estimation
Implementation of spatial spectrum estimation using MUSIC algorithm simulation program, integrated with adaptive Kalman filter optimization techniques.
A comprehensive MATLAB simulation program implementing the MUSIC algorithm for spatial spectrum estimation, featuring detailed code structure and algorithm implementation explanations
MUSIC algorithm for spatial spectrum estimation and DOA (Direction of Arrival) analysis: A power spectrum estimation method based on matrix eigenvalue decomposition comprising two non-parametric estimation approaches - eigenvector estimation and MUSIC estimation. Eigenvector estimation is primarily suitable for power spectrum estimation of sinusoidal signals contaminated with white noise, while MUSIC estimation is more appropriate for general sinusoidal signal parameter estimation. The algorithm implementation involves covariance matrix computation, eigenvalue decomposition, and noise subspace identification for high-resolution spectral estimation.
Implementation and Theoretical Foundation of MUSIC Algorithm for Direction of Arrival Estimation