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.
| Original language | English (US) |
|---|---|
| Title of host publication | 2014 American Control Conference, ACC 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3814-3819 |
| Number of pages | 6 |
| ISBN (Print) | 9781479932726 |
| DOIs | |
| State | Published - 2014 |
| Event | 2014 American Control Conference, ACC 2014 - Portland, OR, United States Duration: Jun 4 2014 → Jun 6 2014 |
Other
| Other | 2014 American Control Conference, ACC 2014 |
|---|---|
| Country | United States |
| City | Portland, OR |
| Period | 6/4/14 → 6/6/14 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
Cite this
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Performance robustness of feature extraction for target detection & classification. / Smith, Brian M.; Chattopadhyay, Pritthi; Ray, Asok; Phoha, Shashi; Damarla, Thyagaraju.
2014 American Control Conference, ACC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3814-3819 6858590.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Performance robustness of feature extraction for target detection & classification
AU - Smith, Brian M.
AU - Chattopadhyay, Pritthi
AU - Ray, Asok
AU - Phoha, Shashi
AU - Damarla, Thyagaraju
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84905675250&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905675250&partnerID=8YFLogxK
U2 - 10.1109/ACC.2014.6858590
DO - 10.1109/ACC.2014.6858590
M3 - Conference contribution
SN - 9781479932726
SP - 3814
EP - 3819
BT - 2014 American Control Conference, ACC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -