Title
Performance robustness of feature extraction for target detection & classification
Abstract
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.
Year
DOI
Venue
2014
10.1109/ACC.2014.6858590
American Control Conference
Keywords
Field
DocType
cepstral analysis,feature extraction,object detection,pattern classification,principal component analysis,sensors,weapons,acoustic sensing systems,automated target classification,automated target detection,cepstrum,environmental uncertainties,feature extraction,mortar launchers,performance robustness,principal component analysis,symbolic dynamic filtering,Feature Extraction,Pattern Classification,Robustness to Environmental Uncertainties
Data mining,Data set,Pattern recognition,Feature (computer vision),Computer science,Cepstrum,Filter (signal processing),Robustness (computer science),Feature extraction,Test data,Artificial intelligence,Principal component analysis
Conference
ISSN
Citations 
PageRank 
0743-1619
2
0.38
References 
Authors
7
4
Name
Order
Citations
PageRank
Smith, B.M.120.38
Chattopadhyay, P.220.71
Ray, A.3832184.32
S. Phoha4102.00