Power Spectrum Estimation Using Higher-Order Cumulants in MATLAB

This document presents the implementation of power spectrum estimation using higher-order cumulants method in MATLAB. Power spectrum estimation is a fundamental signal processing technique for analyzing frequency components and energy distribution of signals. By employing higher-order cumulants (typically third-order or fourth-order statistics), this approach yields more accurate and refined power spectrum estimates compared to conventional methods, effectively handling non-Gaussian signals and mitigating Gaussian noise interference. The implementation involves key MATLAB functions including cumulant calculation routines, FFT-based spectral transformation, and statistical moment processing algorithms. This methodology finds applications across multiple domains such as communication systems (for signal detection and modulation recognition), radar signal processing (target characterization), and biomedical engineering (bio-signal analysis). Through MATLAB's computational environment, users can efficiently implement this advanced technique using matrix operations and statistical toolboxes. This document provides practical guidance on implementing higher-order cumulants-based power spectrum estimation, enabling integration into custom signal processing workflows for enhanced spectral analysis capabilities.