MATLAB Implementation of Weak Signal Detection Using Autocorrelation Algorithm

This article presents a MATLAB-implemented program designed for weak signal detection based on autocorrelation algorithms. The program employs core signal processing techniques including autocorrelation function computation (using xcorr() or custom implementations), noise filtering mechanisms, and threshold-based peak detection to extract subtle signal characteristics from noisy environments. Key implementation aspects involve segmenting input signals into overlapping frames, computing autocorrelation lags to identify periodic patterns, and applying statistical methods to distinguish genuine signals from background noise. The modular architecture allows users to customize parameters such as window sizes, lag ranges, and detection sensitivity through configurable variables. As a MATLAB-based solution, the program supports full code modification and integration with additional toolboxes (e.g., Signal Processing Toolbox) for extended functionality. This implementation serves as a practical tool for analyzing weak signal properties in applications like biomedical sensing, radar systems, and seismic monitoring, providing both educational value and adaptability for research-specific requirements.