Higher-Order Spectral Analysis Toolbox

I strongly recommend utilizing this Higher-Order Spectral Analysis Toolbox, which comprises 58 specialized functions that implement virtually all aspects of higher-order spectral estimation. Higher-order spectral estimation represents an advanced signal processing methodology particularly effective for analyzing spectral characteristics of non-stationary signals, such as time-series data. The toolbox contains implementations of key algorithms including direct and indirect methods for bispectrum estimation, parametric modeling approaches, and nonlinear system identification techniques. Compared to conventional spectral analysis methods, higher-order statistics provide superior accuracy and reliability by incorporating third-order and fourth-order moment information, enabling detection of phase relationships and nonlinear interactions in signals. The MATLAB-based functions in this toolbox facilitate comprehensive signal investigation through features like higher-order cumulant calculation, polyspectral analysis, and time-frequency distributions. This toolbox enables researchers to perform sophisticated signal analysis operations including: - Bispectral density estimation using FFT-based algorithms - Higher-order coherence analysis for system identification - Nonlinear signal detection and characterization - Gaussianity tests using higher-order statistics I have shared this resource exclusively for academic exchange purposes, hoping to provide valuable tools for mastering higher-order spectral estimation techniques. I encourage collaborative learning, knowledge sharing, and technical discussions to foster mutual advancement in signal processing research and applications.