Performance robustness of feature extraction for target detection & classification

Brian M. Smith, Pritthi Chattopadhyay, Asok Ray, Shashi Phoha, Thyagaraju Damarla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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 languageEnglish (US)
Title of host publication2014 American Control Conference, ACC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3814-3819
Number of pages6
ISBN (Print)9781479932726
DOIs
StatePublished - 2014
Event2014 American Control Conference, ACC 2014 - Portland, OR, United States
Duration: Jun 4 2014Jun 6 2014

Other

Other2014 American Control Conference, ACC 2014
CountryUnited States
CityPortland, OR
Period6/4/146/6/14

Fingerprint

Target tracking
Feature extraction
Testing
Mortar
Principal component analysis
Dynamical systems
Acoustics
Uncertainty

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Smith, B. M., Chattopadhyay, P., Ray, A., Phoha, S., & Damarla, T. (2014). Performance robustness of feature extraction for target detection & classification. In 2014 American Control Conference, ACC 2014 (pp. 3814-3819). [6858590] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2014.6858590
Smith, Brian M. ; Chattopadhyay, Pritthi ; Ray, Asok ; Phoha, Shashi ; Damarla, Thyagaraju. / Performance robustness of feature extraction for target detection & classification. 2014 American Control Conference, ACC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 3814-3819
@inproceedings{802e8b51fd8a444da498d463a7a19396,
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.",
author = "Smith, {Brian M.} and Pritthi Chattopadhyay and Asok Ray and Shashi Phoha and Thyagaraju Damarla",
year = "2014",
doi = "10.1109/ACC.2014.6858590",
language = "English (US)",
isbn = "9781479932726",
pages = "3814--3819",
booktitle = "2014 American Control Conference, ACC 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Smith, BM, Chattopadhyay, P, Ray, A, Phoha, S & Damarla, T 2014, Performance robustness of feature extraction for target detection & classification. in 2014 American Control Conference, ACC 2014., 6858590, Institute of Electrical and Electronics Engineers Inc., pp. 3814-3819, 2014 American Control Conference, ACC 2014, Portland, OR, United States, 6/4/14. https://doi.org/10.1109/ACC.2014.6858590

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 proceedingConference 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 -

Smith BM, Chattopadhyay P, Ray A, Phoha S, Damarla T. Performance robustness of feature extraction for target detection & classification. In 2014 American Control Conference, ACC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 3814-3819. 6858590 https://doi.org/10.1109/ACC.2014.6858590