Robust principal component analysis for micro-doppler based automatic target recognition

Research output: Contribution to conferencePaper

Abstract

Dealing with real data it is likely that it will exhibit the presence of unexpected observations within the data which can affect the correct reduction of the representative features of a target signature. For the speciffc case of micro-Doppler based classiffcation this problem can appear in the feature selection stage. To address this problem the Robust PCA based on the Minimum Covariance Determinant (MCD) estimator is introduced. The proposed technique showed to improve the overall classiffcation accuracy.
LanguageEnglish
Number of pages6
Publication statusPublished - Oct 2013
Event3rd IMA conference on Mathematics in Defence - Malvern, United Kingdom
Duration: 24 Oct 2013 → …

Conference

Conference3rd IMA conference on Mathematics in Defence
CountryUnited Kingdom
CityMalvern
Period24/10/13 → …

Fingerprint

Automatic target recognition
Principal component analysis
Feature extraction

Keywords

  • micro-doppler
  • automatic target recognition
  • target classification
  • radar detection

Cite this

Clemente, C., Miller, A. W., & Soraghan, J. J. (2013). Robust principal component analysis for micro-doppler based automatic target recognition. Paper presented at 3rd IMA conference on Mathematics in Defence, Malvern, United Kingdom.
Clemente, C. ; Miller, A.W. ; Soraghan, J.J. / Robust principal component analysis for micro-doppler based automatic target recognition. Paper presented at 3rd IMA conference on Mathematics in Defence, Malvern, United Kingdom.6 p.
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Clemente, C, Miller, AW & Soraghan, JJ 2013, 'Robust principal component analysis for micro-doppler based automatic target recognition' Paper presented at 3rd IMA conference on Mathematics in Defence, Malvern, United Kingdom, 24/10/13, .

Robust principal component analysis for micro-doppler based automatic target recognition. / Clemente, C.; Miller, A.W.; Soraghan, J.J.

2013. Paper presented at 3rd IMA conference on Mathematics in Defence, Malvern, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Robust principal component analysis for micro-doppler based automatic target recognition

AU - Clemente, C.

AU - Miller, A.W.

AU - Soraghan, J.J.

PY - 2013/10

Y1 - 2013/10

N2 - Dealing with real data it is likely that it will exhibit the presence of unexpected observations within the data which can affect the correct reduction of the representative features of a target signature. For the speciffc case of micro-Doppler based classiffcation this problem can appear in the feature selection stage. To address this problem the Robust PCA based on the Minimum Covariance Determinant (MCD) estimator is introduced. The proposed technique showed to improve the overall classiffcation accuracy.

AB - Dealing with real data it is likely that it will exhibit the presence of unexpected observations within the data which can affect the correct reduction of the representative features of a target signature. For the speciffc case of micro-Doppler based classiffcation this problem can appear in the feature selection stage. To address this problem the Robust PCA based on the Minimum Covariance Determinant (MCD) estimator is introduced. The proposed technique showed to improve the overall classiffcation accuracy.

KW - micro-doppler

KW - automatic target recognition

KW - target classification

KW - radar detection

UR - http://www.ima.org.uk/conferences/conferences_calendar/3rd_mathematics_in_defence.cfm.html

M3 - Paper

ER -

Clemente C, Miller AW, Soraghan JJ. Robust principal component analysis for micro-doppler based automatic target recognition. 2013. Paper presented at 3rd IMA conference on Mathematics in Defence, Malvern, United Kingdom.