Robust Model-Based Signal Analysis and Identification

David Pycock, Sridhar Pammu, Amanda Goode, SA Harman

Research output: Contribution to journalArticle

3 Citations (Scopus)


We describe and evaluate a model-based scheme for feature extraction and model-based signal identification which uses likelihood criteria for "edge" detection. Likelihood measures from the feature identification process are shown to provide a well behaved measure of signal interpretation confidence. We demonstrate that complex, transient signals, from one of 6 classes, can reliably be identified at signal to noise ratios of 2 and that identification does not fail until the signal to noise ratio has reached 1. Results show that the loss in identification performance resulting from the use of a heuristic, rather than an exhaustive, search strategy is minimal. Crown Copyright (C) 2001 Published by Elsevier Science Ltd on behalf of the Pattern Recognition Society. All rights reserved.
Original languageEnglish
Pages (from-to)2181-2199
Number of pages19
JournalPattern Recognition
Issue number11
Publication statusPublished - 1 Nov 2001


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