Signal Frequency Estimation Using MVDR Method with Singular Value Decomposition

This MATLAB code implements signal frequency estimation using the MVDR (Minimum Variance Distortionless Response) method combined with Singular Value Decomposition (SVD). The approach represents a modern signal processing technique that accurately estimates signal frequency components through matrix decomposition techniques. The implementation follows a structured pipeline: First, the code performs comprehensive signal preprocessing including noise removal and filtering operations to enhance signal quality. The core algorithm then applies the MVDR method with SVD decomposition, where the signal covariance matrix undergoes singular value decomposition to extract dominant frequency components. Key computational steps include: - Covariance matrix estimation from input signals - SVD implementation using MATLAB's built-in svd() function - Eigenvalue analysis to identify significant frequency components - MVDR spectrum calculation for frequency estimation Finally, the results are visualized through frequency spectrum plots and component analysis graphs, providing intuitive understanding of signal frequency characteristics. The code includes customizable parameters for filter design, SVD threshold settings, and visualization options to accommodate various signal types. This implementation demonstrates robust frequency estimation capabilities suitable for applications in communication systems, radar signal processing, and spectral analysis.