Camp Beale Live-Site UXO Data Inversion and Classification Using Advanced EMI Models
The advanced EMI and statistical classification models are applied to the cued data sets of the Metal Mapper and two next-generation portable sensors: MPV and 2x2 3D TEMTADS. The advanced models combine: (1) the joint diagonalization (JD) algorithm for estimating the number of potential anomalies fr...
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ftdtic:ADA554384 2023-05-15T15:55:50+02:00 Camp Beale Live-Site UXO Data Inversion and Classification Using Advanced EMI Models Shubitidze, Fridon Shamatava, Irma Miller, Jon Keranen, Joe Bijamov, Alex Barrowes, Benjamin DARTMOUGH COLLEGE HANOVER NH THAYER SCHOOL OF ENGINEERING 2011-11 text/html http://www.dtic.mil/docs/citations/ADA554384 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA554384 en eng http://www.dtic.mil/docs/citations/ADA554384 Approved for public release; distribution is unlimited. DTIC Magnetic & Electric Fld Detection & Detectors Ammunition and Explosives *EXPLOSIVES DETECTION *UNEXPLODED AMMUNITION EIGENVALUES ELECTROMAGNETIC INTERFERENCE MAGNETIC ANOMALY DETECTION SYMPOSIA BRIEFING CHARTS ONVMS(ORTHONORMALIZED VOLUME MAGNETIC SOURCE) GAUSSIAN MIXTURE ALGORITHM JD(JOINT DIAGONALIZATION) ALGORITHM UXO(UNEXPLODED ORDNANCE) CAMP BEALE(CALIFORNIA) UXO CLASSIFICATION Text 2011 ftdtic 2016-02-24T07:42:30Z The advanced EMI and statistical classification models are applied to the cued data sets of the Metal Mapper and two next-generation portable sensors: MPV and 2x2 3D TEMTADS. The advanced models combine: (1) the joint diagonalization (JD) algorithm for estimating the number of potential anomalies from the measured data without inversion, (2) the orthonormalized volume magnetic source (ONVMS) model for representing the EMI responses and extracting the intrinsic parameters (feature vector) of the targets, and (3) the Gaussian Mixture algorithm that utilizes the extracted features to classify buried objects as targets of interest (TOI) or not. The inversion and classification schemes of these advanced models consist of the following steps: (i) build the multi-static-response (MSR) data matrix by combining the Tx and Rx data points of the advanced sensors; (ii) apply the JD to the MSR data matrix to determine its eigenvalues; (iii) estimate the data quality and the number of potential targets, based on the eigenvalues; (iv) study the temporal decay of the eigenvalues to identify the signal to noise ratio (SNR); (v) invert all data sets using the ONVMS-Differential Evolution algorithm; (vi) apply the semi-supervised GM clustering algorithm to the inverted total ONVMS to determine the clusters of anomalies; (vii) select anomalies from each cluster to build a custom training list (viii) request the ground truth for the selected targets; (ix) use the obtained ground truth to score the unknown targets using the GM weights for the ONVMS clusters; and (x) submit the final dig-list to the ESTCP office for independent scoring. In this presentation the data inversion processing and discrimination schemes of the advanced EMI models will be reviewed, and the classification results scored by the Institute for Defense Analyses (IDA) will be presented for Camp Beale, CA cued data sets of both MM and portable EMI sensors. Prepared in collaboration with the US Army Cold Regions Research and Engineering Laboratory (CRREL) and Sky Research. Presented at the Partners in Environmental Technology Technical Symposium & Workshop, Washington, DC, 29 Nov-1 Dec 2011. Sponsored by SERDP and ESTCP. U.S. Government or Federal Rights License Text Cold Regions Research and Engineering Laboratory Defense Technical Information Center: DTIC Technical Reports database Beale ENVELOPE(162.750,162.750,-66.567,-66.567) Ida ENVELOPE(170.483,170.483,-83.583,-83.583) |
institution |
Open Polar |
collection |
Defense Technical Information Center: DTIC Technical Reports database |
op_collection_id |
ftdtic |
language |
English |
topic |
Magnetic & Electric Fld Detection & Detectors Ammunition and Explosives *EXPLOSIVES DETECTION *UNEXPLODED AMMUNITION EIGENVALUES ELECTROMAGNETIC INTERFERENCE MAGNETIC ANOMALY DETECTION SYMPOSIA BRIEFING CHARTS ONVMS(ORTHONORMALIZED VOLUME MAGNETIC SOURCE) GAUSSIAN MIXTURE ALGORITHM JD(JOINT DIAGONALIZATION) ALGORITHM UXO(UNEXPLODED ORDNANCE) CAMP BEALE(CALIFORNIA) UXO CLASSIFICATION |
spellingShingle |
Magnetic & Electric Fld Detection & Detectors Ammunition and Explosives *EXPLOSIVES DETECTION *UNEXPLODED AMMUNITION EIGENVALUES ELECTROMAGNETIC INTERFERENCE MAGNETIC ANOMALY DETECTION SYMPOSIA BRIEFING CHARTS ONVMS(ORTHONORMALIZED VOLUME MAGNETIC SOURCE) GAUSSIAN MIXTURE ALGORITHM JD(JOINT DIAGONALIZATION) ALGORITHM UXO(UNEXPLODED ORDNANCE) CAMP BEALE(CALIFORNIA) UXO CLASSIFICATION Shubitidze, Fridon Shamatava, Irma Miller, Jon Keranen, Joe Bijamov, Alex Barrowes, Benjamin Camp Beale Live-Site UXO Data Inversion and Classification Using Advanced EMI Models |
topic_facet |
Magnetic & Electric Fld Detection & Detectors Ammunition and Explosives *EXPLOSIVES DETECTION *UNEXPLODED AMMUNITION EIGENVALUES ELECTROMAGNETIC INTERFERENCE MAGNETIC ANOMALY DETECTION SYMPOSIA BRIEFING CHARTS ONVMS(ORTHONORMALIZED VOLUME MAGNETIC SOURCE) GAUSSIAN MIXTURE ALGORITHM JD(JOINT DIAGONALIZATION) ALGORITHM UXO(UNEXPLODED ORDNANCE) CAMP BEALE(CALIFORNIA) UXO CLASSIFICATION |
description |
The advanced EMI and statistical classification models are applied to the cued data sets of the Metal Mapper and two next-generation portable sensors: MPV and 2x2 3D TEMTADS. The advanced models combine: (1) the joint diagonalization (JD) algorithm for estimating the number of potential anomalies from the measured data without inversion, (2) the orthonormalized volume magnetic source (ONVMS) model for representing the EMI responses and extracting the intrinsic parameters (feature vector) of the targets, and (3) the Gaussian Mixture algorithm that utilizes the extracted features to classify buried objects as targets of interest (TOI) or not. The inversion and classification schemes of these advanced models consist of the following steps: (i) build the multi-static-response (MSR) data matrix by combining the Tx and Rx data points of the advanced sensors; (ii) apply the JD to the MSR data matrix to determine its eigenvalues; (iii) estimate the data quality and the number of potential targets, based on the eigenvalues; (iv) study the temporal decay of the eigenvalues to identify the signal to noise ratio (SNR); (v) invert all data sets using the ONVMS-Differential Evolution algorithm; (vi) apply the semi-supervised GM clustering algorithm to the inverted total ONVMS to determine the clusters of anomalies; (vii) select anomalies from each cluster to build a custom training list (viii) request the ground truth for the selected targets; (ix) use the obtained ground truth to score the unknown targets using the GM weights for the ONVMS clusters; and (x) submit the final dig-list to the ESTCP office for independent scoring. In this presentation the data inversion processing and discrimination schemes of the advanced EMI models will be reviewed, and the classification results scored by the Institute for Defense Analyses (IDA) will be presented for Camp Beale, CA cued data sets of both MM and portable EMI sensors. Prepared in collaboration with the US Army Cold Regions Research and Engineering Laboratory (CRREL) and Sky Research. Presented at the Partners in Environmental Technology Technical Symposium & Workshop, Washington, DC, 29 Nov-1 Dec 2011. Sponsored by SERDP and ESTCP. U.S. Government or Federal Rights License |
author2 |
DARTMOUGH COLLEGE HANOVER NH THAYER SCHOOL OF ENGINEERING |
format |
Text |
author |
Shubitidze, Fridon Shamatava, Irma Miller, Jon Keranen, Joe Bijamov, Alex Barrowes, Benjamin |
author_facet |
Shubitidze, Fridon Shamatava, Irma Miller, Jon Keranen, Joe Bijamov, Alex Barrowes, Benjamin |
author_sort |
Shubitidze, Fridon |
title |
Camp Beale Live-Site UXO Data Inversion and Classification Using Advanced EMI Models |
title_short |
Camp Beale Live-Site UXO Data Inversion and Classification Using Advanced EMI Models |
title_full |
Camp Beale Live-Site UXO Data Inversion and Classification Using Advanced EMI Models |
title_fullStr |
Camp Beale Live-Site UXO Data Inversion and Classification Using Advanced EMI Models |
title_full_unstemmed |
Camp Beale Live-Site UXO Data Inversion and Classification Using Advanced EMI Models |
title_sort |
camp beale live-site uxo data inversion and classification using advanced emi models |
publishDate |
2011 |
url |
http://www.dtic.mil/docs/citations/ADA554384 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA554384 |
long_lat |
ENVELOPE(162.750,162.750,-66.567,-66.567) ENVELOPE(170.483,170.483,-83.583,-83.583) |
geographic |
Beale Ida |
geographic_facet |
Beale Ida |
genre |
Cold Regions Research and Engineering Laboratory |
genre_facet |
Cold Regions Research and Engineering Laboratory |
op_source |
DTIC |
op_relation |
http://www.dtic.mil/docs/citations/ADA554384 |
op_rights |
Approved for public release; distribution is unlimited. |
_version_ |
1766391323635482624 |