Parametric and Non-Parametric Spectrum Detection Algorithms

This document explores various spectrum detection algorithms based on both parametric and non-parametric methodologies, accompanied by in-depth simulation analysis. Parametric approaches typically involve model-based techniques such as autoregressive (AR) modeling and eigenvalue decomposition, which require prior knowledge about signal characteristics. Non-parametric methods include periodogram-based techniques and Welch's method that rely on direct Fourier transform calculations without assuming specific signal models.

Through comparative simulations implemented in MATLAB/Python, we evaluate algorithm performance metrics including detection probability, false alarm rate, and computational complexity. The implementation code typically involves signal preprocessing, feature extraction using functions like pwelch() for power spectral density estimation, and threshold optimization for detection decisions. This research helps identify optimal algorithm selection for different application scenarios such as cognitive radio systems, radar signal processing, and spectrum monitoring.

We provide detailed explanations of algorithm principles and implementation architectures, including key computational blocks like covariance matrix calculation for parametric methods and windowing functions for non-parametric approaches. The simulation results analysis discusses practical implementation considerations, including computational resource requirements and real-time processing capabilities. Finally, we propose potential enhancements such as hybrid detection schemes and machine learning-based threshold adaptation to improve detection performance in low SNR environments.