This system uses correntropy-based signal processing to separate noise components from information-carrying components, enabling detection of low-level periodic signals within noisy signals. Detecting weak signals in noisy environments is a key challenge for many communications, surveillance, or monitoring systems. Signal processors use a variety of filtering techniques to separate noise components from information-carrying components, but these vary in effectiveness. Principal component analysis (PCA) is a common filtering procedure that decomposes a signal into multiple principle components, separating out the information-carrying components. However, standard PCA is data-dependent, requiring external data. Therefore, it may not effectively separate the information-carrying signal from the noise, depending on the components and available signal data.
Researchers at the University of Florida have developed a data-independent signal processing system that uses a correntropy function to generate a nonlinear autocorrelation matrix. The system may then apply temporal PCA to separate the signal components and analyze them based on energy levels without the need for external data.
Signal processing system that detects information-carrying signals in high noise environments
This signal processing system generates a nonlinear mapping of a highly noisy signal through use of correntropy, a nonlinear measure of similarity. A data-independent correntropy kernel generates a nonlinear signal mapping that in turn generates a nonlinear autocorrelation matrix for subsequent use in temporal principal component analysis. After selecting the principal component with the highest energy, the system performs power spectral density (PSD) analysis on it. The maximum peak corresponds to the noise-obscured, information-carrying signal, which may then filter out for interpretation. Alternatively, the system can perform a PSD analysis on any individual component in order to extract a signal of interest.
