Extended Object Tracking With Automotive Radar Using B-Spline Chained Ellipses Model

This paper introduces a B-spline chained ellipses model representation for extended object tracking (EOT) using high-resolution automotive radar measurements. With offline automotive radar training datasets, the proposed model parameters are learned using the expectation-maximization (EM) algorithm....

Full description

Saved in:
Bibliographic Details
Main Authors: Yao, G., Wang, P., Berntorp, K., Mansour, H., Boufounos, P., Orlik, P. V.
Format: Conference Proceeding
Language:English
Subjects:
automotive radar
B-spline
Extended object tracking
Numerical models
PMHT
Radar
Radar measurements
Radar tracking
random matrix model
Signal processing algorithms
Training
Transforms
unscented transform
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 8412
container_issue
container_start_page 8408
container_title
container_volume
creator Yao, G.
Wang, P.
Berntorp, K.
Mansour, H.
Boufounos, P.
Orlik, P. V.
description This paper introduces a B-spline chained ellipses model representation for extended object tracking (EOT) using high-resolution automotive radar measurements. With offline automotive radar training datasets, the proposed model parameters are learned using the expectation-maximization (EM) algorithm. Then the probabilistic multi-hypothesis tracking (PMHT) along with the unscented transform (UT) is proposed to deal with the nonlinear forward-warping coordinate transformation, the measurement-to-ellipsis association, and the state update step. Numerical validation is provided to verify the effectiveness of the proposed EOT framework with automotive radar measurements.
doi_str_mv 10.1109/ICASSP39728.2021.9415080
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9415080</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9415080</ieee_id><sourcerecordid>9415080</sourcerecordid><originalsourceid>FETCH-LOGICAL-i203t-f7fed916690880671670baf3afdbf1a0f5fc221c9d0502ea018103de780c6c9e3</originalsourceid><addsrcrecordid>eNotkFFLwzAUhaMgOOd-gS_5A633JmuTPM5SnTCZ2A59G2lz4zK7brRV9N87cU8fnAMfh8MYR4gRwdw-ZrOieJZGCR0LEBibKSag4YxNjNJ4jFGlkCTnbCSkMhEaeLtkV32_BQCtpnrEyvx7oNaR48tqS_XAy87WH6F9569h2PDZ57Df7YfwRfzFOtvxVf_X3UXFoQkt8Wxjj3A8b5pw6KnnT3tHzTW78LbpaXLimK3u8zKbR4vlw3HzIgoC5BB55ckZTFMDWkOqMFVQWS-td5VHCz7xtRBYGwcJCLKAGkE6UhrqtDYkx-zm3xuIaH3ows52P-vTCfIXaeRR5g</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Extended Object Tracking With Automotive Radar Using B-Spline Chained Ellipses Model</title><source>IEEE Xplore All Conference Series</source><creator>Yao, G. ; Wang, P. ; Berntorp, K. ; Mansour, H. ; Boufounos, P. ; Orlik, P. V.</creator><creatorcontrib>Yao, G. ; Wang, P. ; Berntorp, K. ; Mansour, H. ; Boufounos, P. ; Orlik, P. V.</creatorcontrib><description>This paper introduces a B-spline chained ellipses model representation for extended object tracking (EOT) using high-resolution automotive radar measurements. With offline automotive radar training datasets, the proposed model parameters are learned using the expectation-maximization (EM) algorithm. Then the probabilistic multi-hypothesis tracking (PMHT) along with the unscented transform (UT) is proposed to deal with the nonlinear forward-warping coordinate transformation, the measurement-to-ellipsis association, and the state update step. Numerical validation is provided to verify the effectiveness of the proposed EOT framework with automotive radar measurements.</description><identifier>EISSN: 2379-190X</identifier><identifier>EISBN: 9781728176055</identifier><identifier>EISBN: 1728176050</identifier><identifier>DOI: 10.1109/ICASSP39728.2021.9415080</identifier><language>eng</language><publisher>IEEE</publisher><subject>automotive radar ; B-spline ; Extended object tracking ; Numerical models ; PMHT ; Radar ; Radar measurements ; Radar tracking ; random matrix model ; Signal processing algorithms ; Training ; Transforms ; unscented transform</subject><ispartof>ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, p.8408-8412</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/9415080$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9415080$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yao, G.</creatorcontrib><creatorcontrib>Wang, P.</creatorcontrib><creatorcontrib>Berntorp, K.</creatorcontrib><creatorcontrib>Mansour, H.</creatorcontrib><creatorcontrib>Boufounos, P.</creatorcontrib><creatorcontrib>Orlik, P. V.</creatorcontrib><title>Extended Object Tracking With Automotive Radar Using B-Spline Chained Ellipses Model</title><title>ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</title><addtitle>ICASSP</addtitle><description>This paper introduces a B-spline chained ellipses model representation for extended object tracking (EOT) using high-resolution automotive radar measurements. With offline automotive radar training datasets, the proposed model parameters are learned using the expectation-maximization (EM) algorithm. Then the probabilistic multi-hypothesis tracking (PMHT) along with the unscented transform (UT) is proposed to deal with the nonlinear forward-warping coordinate transformation, the measurement-to-ellipsis association, and the state update step. Numerical validation is provided to verify the effectiveness of the proposed EOT framework with automotive radar measurements.</description><subject>automotive radar</subject><subject>B-spline</subject><subject>Extended object tracking</subject><subject>Numerical models</subject><subject>PMHT</subject><subject>Radar</subject><subject>Radar measurements</subject><subject>Radar tracking</subject><subject>random matrix model</subject><subject>Signal processing algorithms</subject><subject>Training</subject><subject>Transforms</subject><subject>unscented transform</subject><issn>2379-190X</issn><isbn>9781728176055</isbn><isbn>1728176050</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2021</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkFFLwzAUhaMgOOd-gS_5A633JmuTPM5SnTCZ2A59G2lz4zK7brRV9N87cU8fnAMfh8MYR4gRwdw-ZrOieJZGCR0LEBibKSag4YxNjNJ4jFGlkCTnbCSkMhEaeLtkV32_BQCtpnrEyvx7oNaR48tqS_XAy87WH6F9569h2PDZ57Df7YfwRfzFOtvxVf_X3UXFoQkt8Wxjj3A8b5pw6KnnT3tHzTW78LbpaXLimK3u8zKbR4vlw3HzIgoC5BB55ckZTFMDWkOqMFVQWS-td5VHCz7xtRBYGwcJCLKAGkE6UhrqtDYkx-zm3xuIaH3ows52P-vTCfIXaeRR5g</recordid><startdate>20210606</startdate><enddate>20210606</enddate><creator>Yao, G.</creator><creator>Wang, P.</creator><creator>Berntorp, K.</creator><creator>Mansour, H.</creator><creator>Boufounos, P.</creator><creator>Orlik, P. V.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20210606</creationdate><title>Extended Object Tracking With Automotive Radar Using B-Spline Chained Ellipses Model</title><author>Yao, G. ; Wang, P. ; Berntorp, K. ; Mansour, H. ; Boufounos, P. ; Orlik, P. V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-f7fed916690880671670baf3afdbf1a0f5fc221c9d0502ea018103de780c6c9e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2021</creationdate><topic>automotive radar</topic><topic>B-spline</topic><topic>Extended object tracking</topic><topic>Numerical models</topic><topic>PMHT</topic><topic>Radar</topic><topic>Radar measurements</topic><topic>Radar tracking</topic><topic>random matrix model</topic><topic>Signal processing algorithms</topic><topic>Training</topic><topic>Transforms</topic><topic>unscented transform</topic><toplevel>online_resources</toplevel><creatorcontrib>Yao, G.</creatorcontrib><creatorcontrib>Wang, P.</creatorcontrib><creatorcontrib>Berntorp, K.</creatorcontrib><creatorcontrib>Mansour, H.</creatorcontrib><creatorcontrib>Boufounos, P.</creatorcontrib><creatorcontrib>Orlik, P. V.</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/IET 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>Yao, G.</au><au>Wang, P.</au><au>Berntorp, K.</au><au>Mansour, H.</au><au>Boufounos, P.</au><au>Orlik, P. V.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Extended Object Tracking With Automotive Radar Using B-Spline Chained Ellipses Model</atitle><btitle>ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</btitle><stitle>ICASSP</stitle><date>2021-06-06</date><risdate>2021</risdate><spage>8408</spage><epage>8412</epage><pages>8408-8412</pages><eissn>2379-190X</eissn><eisbn>9781728176055</eisbn><eisbn>1728176050</eisbn><abstract>This paper introduces a B-spline chained ellipses model representation for extended object tracking (EOT) using high-resolution automotive radar measurements. With offline automotive radar training datasets, the proposed model parameters are learned using the expectation-maximization (EM) algorithm. Then the probabilistic multi-hypothesis tracking (PMHT) along with the unscented transform (UT) is proposed to deal with the nonlinear forward-warping coordinate transformation, the measurement-to-ellipsis association, and the state update step. Numerical validation is provided to verify the effectiveness of the proposed EOT framework with automotive radar measurements.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP39728.2021.9415080</doi><tpages>5</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2379-190X
ispartof ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, p.8408-8412
issn 2379-190X
language eng
recordid cdi_ieee_primary_9415080
source IEEE Xplore All Conference Series
subjects automotive radar
B-spline
Extended object tracking
Numerical models
PMHT
Radar
Radar measurements
Radar tracking
random matrix model
Signal processing algorithms
Training
Transforms
unscented transform
title Extended Object Tracking With Automotive Radar Using B-Spline Chained Ellipses Model
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T16%3A16%3A25IST&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=Extended%20Object%20Tracking%20With%20Automotive%20Radar%20Using%20B-Spline%20Chained%20Ellipses%20Model&rft.btitle=ICASSP%202021%20-%202021%20IEEE%20International%20Conference%20on%20Acoustics,%20Speech%20and%20Signal%20Processing%20(ICASSP)&rft.au=Yao,%20G.&rft.date=2021-06-06&rft.spage=8408&rft.epage=8412&rft.pages=8408-8412&rft.eissn=2379-190X&rft_id=info:doi/10.1109/ICASSP39728.2021.9415080&rft.eisbn=9781728176055&rft.eisbn_list=1728176050&rft_dat=%3Cieee_CHZPO%3E9415080%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i203t-f7fed916690880671670baf3afdbf1a0f5fc221c9d0502ea018103de780c6c9e3%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=9415080&rfr_iscdi=true