Robust model-based signal analysis and identification

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 dynamic, rather than an exhaustive search strategy, is minimal