Performance robustness of feature extraction for target detection & classification

Performance robustness of feature extraction with respect to environmental uncertainties is often critical for automated target detection & classification. This paper focuses on performance robustness in the sense that the extracted features are desired to be largely insensitive to environmental uncertainties, while they should be capable of recognizing the effects of small perturbations in the underlying system dynamics for detection & classification. From this perspective, performance robustness of three feature extraction algorithms, namely, principal component analysis, cepstrum, and symbolic dynamic filtering, is evaluated for target classification by making use of the respective field data collected from different sites. These algorithms have been evaluated for robust classification of two different types of mortar launchers with acoustic sensing systems, based on the training and testing data sets from the same and different field sites. The results, generated with training and testing data from different field sites, characterize performance robustness of the respective feature extraction algorithms, when compared with those generated with the corresponding data sets from the same field site.