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Performance Comparison of Feature Extraction Algorithms for Target Detection and Classification
PENNSYLVANIA STATE UNIV STATE COLLEGE DEPT OF MECHANICAL ENGINEERING
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This paper addresses the problem of target detection and classification, where the performance is often limited due to high rates of false alarm and classification error, possibly because of inadequacies in the underlying algorithms of feature extraction from sensory data and subsequent pattern classification. In this paper, a recently reported feature extraction algorithm, symbolic dynamic filtering SDF, is investigated for target detection and classification by using unmanned ground sensors UGS. In SDF, sensor time series data are first symbolized to construct probabilistic finite state automata PFSA that, in turn, generate low-dimensional feature vectors. In this paper, the performance of SDF is compared with that of two commonly used feature extractors, namely Cepstrum and principal component analysis PCA, for target detection and classification. Three different pattern classifiers have been employed to compare the performance of the three feature extractors for target detection and humananimal classification by UGS systems based on two sets of field data that consist of passive infrared PIR and seismic sensors. The results show consistently superior performance of SDF-based feature extraction over Cepstrum-based and PCA-based feature extraction in terms of successful detection, false alarm, and misclassification rates.
APPROVED FOR PUBLIC RELEASE