A signal detection system based on Dempster-Shafer theory and comparison to fuzzy detection

This paper describes a signal detection algorithm based on Dempster-Shafer theory: The detector combines evidence provided by multiple waveform features and explicitly considers uncertainty in the detection decision, The detector classifies waveforms as including a signal, not including a signal, or being uncertain, in which case no conclusion regarding presence or absence of a signal is drawn. The probability numbers required in the Dempster-Shafer formulation are defined as piece-wise linear functions that can be described by two parameters, and the effects of these parameters on detector performance, using simulated data, are compared to Bayesian detection and to a fuzzy signal detector that also considers uncertainty. The performance of the Dempster-Shafer end fuzzy detectors shows similar dependence on the parameters, although, if parameters are adjusted so that the number of correctly classified waveforms are equal, the Dempster-Shafer detector has more uncertain classifications and fewer errors than the fuzzy detector, providing superior performance. The Dempster-Shafer detector incorporates a different type of uncertainty than the fuzzy detector, which may contribute to this difference in performance. The difference mag also reflect the different mathematical operations used.