Detection and Classification of Highly Noisy Non-Stationary Signals Using Global Dynamical Models

During the past two years, RTA scientists have developed a numerical scheme for the detection and classification (D/C) of signals in highly noisy environments (negative SNR). This method performs D/C using nonlinear signal information, based on techniques developed from the theory of Chaotic dynamics. Preliminary tests on real-world data sets, recently supplied by Navy personnel, indicate that this method will likely outperform most current linear classification methods, provided the signal of interest contains some nonlinear correlations. In addition, this method has been modified to perform D/C on transient signals (e.g. active pulses), with correspondingly good performance. These algorithms are quite computationally efficient, and can be implemented on any standard workstation environment. The current aim of the research program will be to further refine these methods, measure operating characteristics, characterize performance on a variety of real-world data sets, and especially to modify the basic scheme for a variety of other problems of interest to the Navy. Anticipated benefits to the Navy will be electronic signal recognition and classification technologies with significantly improved performance characteristics, especially suited to high noise environments.