Feature extraction by identification of a parameterized system model

This paper focuses on extracting features from time series for pattern recognition. System identification techniques are used to represent the signals by a parameterized system model (PSM) with the parameter vector describing the PSM becoming the feature vector. A deconvolution procedure is used to enhance pattern class discrimination. The advantages of the PSM approach is a reduction of the dimensionality of the feature space thereby simplifying the classifier design and evaluation. The PSM feature extraction technique is applied to a flaw characterization problem arising from ultrasonic nondestructive testing of materials.