TY - JOUR
T1 - Robust Model-Based Signal Analysis and Identification
AU - Pycock, David
AU - Pammu, Sridhar
AU - Goode, Amanda
AU - Harman, SA
PY - 2001/11/1
Y1 - 2001/11/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0035501803&partnerID=8YFLogxK
U2 - 10.1016/S0031-3203(00)00143-6
DO - 10.1016/S0031-3203(00)00143-6
M3 - Article
VL - 34
SP - 2181
EP - 2199
JO - Pattern Recognition
JF - Pattern Recognition
IS - 11
ER -