Deep Learning and Information Geometry for Drone Micro-Doppler Radar Classification

In this work, we build dedicated learning models for micro-Doppler radar time series classification. We develop both deep temporal architectures based on time-frequency representations, and also directly study the signal's underlying statistical Gaussian process using Information Geometry on Ri...

Full description

Saved in:
Bibliographic Details
Main Authors: Brooks, Daniel, Schwander, Olivier, Barbaresco, Frederic, Schneider, Jean -Yves, Cord, Matthieu
Format: Conference Proceeding
Language:English
Subjects:
Computer architecture
Convolution
Manifolds
Neural networks
Pipelines
Radar
Time series analysis
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 6
container_issue
container_start_page 1
container_title
container_volume
creator Brooks, Daniel
Schwander, Olivier
Barbaresco, Frederic
Schneider, Jean -Yves
Cord, Matthieu
description In this work, we build dedicated learning models for micro-Doppler radar time series classification. We develop both deep temporal architectures based on time-frequency representations, and also directly study the signal's underlying statistical Gaussian process using Information Geometry on Riemannian manifolds by developing and improving symmetric positive definite (SPD) neural networks. We also propose the aggregation of all proposed models in a single, highly performing classification pipeline.
doi_str_mv 10.1109/RadarConf2043947.2020.9266689
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9266689</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9266689</ieee_id><sourcerecordid>9266689</sourcerecordid><originalsourceid>FETCH-LOGICAL-h237t-c21c42ba6bd25ba0eea49d5b1f6b6ebe3a20c9b337d579b9141584140f36efd43</originalsourceid><addsrcrecordid>eNotkMtKxDAYhaMgOI7zBG6ycdmae5ultDoOVAQv6yFp_mikTUrazby9wzirAx-cCwehe0pKSol-eDfO5CZFz4jgWlQlI4yUmimlan2BNrqqacVqWmvByCVaMV7JQnJaX6Obef4lRPIjWqGPFmDCHZgcQ_zGJjq8iz7l0SwhRbyFNMKSD_iIcJtTBPwa-pyKNk3TABmfduBmMPMcfOhPrlt05c0ww-asa_T1_PTZvBTd23bXPHbFz7F6KXpGe8GsUdYxaQ0BMEI7aalXVoEFbhjpteW8crLSVlNBZS2oIJ4r8E7wNbr7zw0AsJ9yGE0-7M8f8D_Mf1SC</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Deep Learning and Information Geometry for Drone Micro-Doppler Radar Classification</title><source>IEEE Xplore All Conference Series</source><creator>Brooks, Daniel ; Schwander, Olivier ; Barbaresco, Frederic ; Schneider, Jean -Yves ; Cord, Matthieu</creator><creatorcontrib>Brooks, Daniel ; Schwander, Olivier ; Barbaresco, Frederic ; Schneider, Jean -Yves ; Cord, Matthieu</creatorcontrib><description>In this work, we build dedicated learning models for micro-Doppler radar time series classification. We develop both deep temporal architectures based on time-frequency representations, and also directly study the signal's underlying statistical Gaussian process using Information Geometry on Riemannian manifolds by developing and improving symmetric positive definite (SPD) neural networks. We also propose the aggregation of all proposed models in a single, highly performing classification pipeline.</description><identifier>EISSN: 2375-5318</identifier><identifier>EISBN: 9781728189420</identifier><identifier>EISBN: 172818942X</identifier><identifier>DOI: 10.1109/RadarConf2043947.2020.9266689</identifier><language>eng</language><publisher>IEEE</publisher><subject>Computer architecture ; Convolution ; Manifolds ; Neural networks ; Pipelines ; Radar ; Time series analysis</subject><ispartof>2020 IEEE Radar Conference (RadarConf20), 2020, p.1-6</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9266689$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9266689$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Brooks, Daniel</creatorcontrib><creatorcontrib>Schwander, Olivier</creatorcontrib><creatorcontrib>Barbaresco, Frederic</creatorcontrib><creatorcontrib>Schneider, Jean -Yves</creatorcontrib><creatorcontrib>Cord, Matthieu</creatorcontrib><title>Deep Learning and Information Geometry for Drone Micro-Doppler Radar Classification</title><title>2020 IEEE Radar Conference (RadarConf20)</title><addtitle>RadarConf20</addtitle><description>In this work, we build dedicated learning models for micro-Doppler radar time series classification. We develop both deep temporal architectures based on time-frequency representations, and also directly study the signal's underlying statistical Gaussian process using Information Geometry on Riemannian manifolds by developing and improving symmetric positive definite (SPD) neural networks. We also propose the aggregation of all proposed models in a single, highly performing classification pipeline.</description><subject>Computer architecture</subject><subject>Convolution</subject><subject>Manifolds</subject><subject>Neural networks</subject><subject>Pipelines</subject><subject>Radar</subject><subject>Time series analysis</subject><issn>2375-5318</issn><isbn>9781728189420</isbn><isbn>172818942X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2020</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkMtKxDAYhaMgOI7zBG6ycdmae5ultDoOVAQv6yFp_mikTUrazby9wzirAx-cCwehe0pKSol-eDfO5CZFz4jgWlQlI4yUmimlan2BNrqqacVqWmvByCVaMV7JQnJaX6Obef4lRPIjWqGPFmDCHZgcQ_zGJjq8iz7l0SwhRbyFNMKSD_iIcJtTBPwa-pyKNk3TABmfduBmMPMcfOhPrlt05c0ww-asa_T1_PTZvBTd23bXPHbFz7F6KXpGe8GsUdYxaQ0BMEI7aalXVoEFbhjpteW8crLSVlNBZS2oIJ4r8E7wNbr7zw0AsJ9yGE0-7M8f8D_Mf1SC</recordid><startdate>20200921</startdate><enddate>20200921</enddate><creator>Brooks, Daniel</creator><creator>Schwander, Olivier</creator><creator>Barbaresco, Frederic</creator><creator>Schneider, Jean -Yves</creator><creator>Cord, Matthieu</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20200921</creationdate><title>Deep Learning and Information Geometry for Drone Micro-Doppler Radar Classification</title><author>Brooks, Daniel ; Schwander, Olivier ; Barbaresco, Frederic ; Schneider, Jean -Yves ; Cord, Matthieu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-h237t-c21c42ba6bd25ba0eea49d5b1f6b6ebe3a20c9b337d579b9141584140f36efd43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer architecture</topic><topic>Convolution</topic><topic>Manifolds</topic><topic>Neural networks</topic><topic>Pipelines</topic><topic>Radar</topic><topic>Time series analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Brooks, Daniel</creatorcontrib><creatorcontrib>Schwander, Olivier</creatorcontrib><creatorcontrib>Barbaresco, Frederic</creatorcontrib><creatorcontrib>Schneider, Jean -Yves</creatorcontrib><creatorcontrib>Cord, Matthieu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Brooks, Daniel</au><au>Schwander, Olivier</au><au>Barbaresco, Frederic</au><au>Schneider, Jean -Yves</au><au>Cord, Matthieu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Deep Learning and Information Geometry for Drone Micro-Doppler Radar Classification</atitle><btitle>2020 IEEE Radar Conference (RadarConf20)</btitle><stitle>RadarConf20</stitle><date>2020-09-21</date><risdate>2020</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><eissn>2375-5318</eissn><eisbn>9781728189420</eisbn><eisbn>172818942X</eisbn><abstract>In this work, we build dedicated learning models for micro-Doppler radar time series classification. We develop both deep temporal architectures based on time-frequency representations, and also directly study the signal's underlying statistical Gaussian process using Information Geometry on Riemannian manifolds by developing and improving symmetric positive definite (SPD) neural networks. We also propose the aggregation of all proposed models in a single, highly performing classification pipeline.</abstract><pub>IEEE</pub><doi>10.1109/RadarConf2043947.2020.9266689</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2375-5318
ispartof 2020 IEEE Radar Conference (RadarConf20), 2020, p.1-6
issn 2375-5318
language eng
recordid cdi_ieee_primary_9266689
source IEEE Xplore All Conference Series
subjects Computer architecture
Convolution
Manifolds
Neural networks
Pipelines
Radar
Time series analysis
title Deep Learning and Information Geometry for Drone Micro-Doppler Radar Classification
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T20%3A12%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Deep%20Learning%20and%20Information%20Geometry%20for%20Drone%20Micro-Doppler%20Radar%20Classification&rft.btitle=2020%20IEEE%20Radar%20Conference%20(RadarConf20)&rft.au=Brooks,%20Daniel&rft.date=2020-09-21&rft.spage=1&rft.epage=6&rft.pages=1-6&rft.eissn=2375-5318&rft_id=info:doi/10.1109/RadarConf2043947.2020.9266689&rft.eisbn=9781728189420&rft.eisbn_list=172818942X&rft_dat=%3Cieee_CHZPO%3E9266689%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-h237t-c21c42ba6bd25ba0eea49d5b1f6b6ebe3a20c9b337d579b9141584140f36efd43%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9266689&rfr_iscdi=true