Correlation Detection of Noisy Pulse Signals
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Resource Overview
Perform correlation detection on given noisy pulse signals, with detection results represented as 1s and 0s. The implementation involves reading data from text files and analyzing statistical characteristics of signals before and after filtering, including mean, variance, autocorrelation, and power spectrum. Design reference signals for correlation processing and detection. Plot both original data curves and correlation result curves to visualize signal transformations.
Detailed Documentation
Implement correlation detection on noisy pulse signals, where detection outcomes are encoded as binary values (1/0). The algorithm requires loading signal data from text files and comparing statistical properties pre- and post-filtering through calculations of mean, variance, autocorrelation, and power spectral density. Key implementation steps include designing matched reference signals using cross-correlation techniques, applying threshold-based detection algorithms, and visualizing results through MATLAB's plotting functions (e.g., plot(), xcorr(), pwelch()). The workflow generates comparative graphs of raw signals versus processed correlation outputs, enabling intuitive observation of noise reduction and signal enhancement effects. Code structure typically involves signal preprocessing, statistical feature extraction, correlation coefficient computation, and dynamic threshold adjustment for optimal detection accuracy.
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