MATLAB Prony Algorithm Based on SVD-TLS Total Least Squares Method

This article discusses the "MATLAB Prony algorithm based on SVD-TLS total least squares," which is an algorithm rooted in total least squares methodology primarily used for signal processing and predictive analysis. The algorithm estimates signal frequencies by fitting peaks within signals, enabling comprehensive signal analysis. Specifically, it performs Singular Value Decomposition (SVD) on the input signal data, then applies the Total Least Squares (TLS) algorithm for curve fitting to derive predictive values. From an implementation perspective, the algorithm typically involves constructing a Hankel matrix from the signal data, performing SVD to identify significant components and determine the model order, and then solving the TLS problem to obtain the polynomial coefficients. Key MATLAB functions that may be employed include svd() for matrix decomposition, and specialized TLS solvers for the parameter estimation phase. This algorithm proves particularly valuable in signal processing and predictive analysis applications across various domains including medical imaging, engineering systems, and statistical modeling, especially when dealing with noisy data where traditional least squares methods may underperform.