Title :
Neural network based novelty filtering for signal detection enhancement
Author :
Ko, Hanseok ; Baran, Robert ; Arozullah, Mohammed
Author_Institution :
US Naval Surface Warfare Center, Silver Spring, MD, USA
Abstract :
Discusses the analytical basis for designing an adaptive novelty filter (ANF) based on a multilayer feedforward neural network in order to enhance the detectability of weak, transient signals in the presence of comparatively high-level background noise or interference which has unknown, uncharacterized, or time-varying statistical properties. The ANF serves as a front end preprocessor to any device which performs signal detection, estimation or classification. The ideal ANF would selectively filter out the noise while passing the signal without attenuation or distortion. The conditions under which the novelty filtering effect is most pronounced are presented
Keywords :
adaptive filters; feedforward neural nets; filtering and prediction theory; signal detection; ANF; adaptive novelty filter; background noise; classification; estimation; front end preprocessor; multilayer feedforward neural network; signal detection enhancement; time-varying statistical properties; transient signals; Adaptive filters; Adaptive signal detection; Feedforward neural networks; Filtering; Multi-layer neural network; Neural networks; Signal analysis; Signal design; Signal detection; Transient analysis;
Conference_Titel :
Circuits and Systems, 1992., Proceedings of the 35th Midwest Symposium on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-0510-8
DOI :
10.1109/MWSCAS.1992.271387
