Vehicle Speed Estimation Using Acoustic Wave Patterns
We estimate a vehicle's speed, its wheelbase length, and tire track length by jointly estimating its acoustic wave pattern with a single passive acoustic sensor that records the vehicle's drive-by noise. The acoustic wave pattern is determined using the vehicle's speed, the Doppler sh...
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| Veröffentlicht in: | IEEE transactions on signal processing 2009-01, Vol.57 (1), p.30-47 |
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| creator | Cevher, V. Chellappa, R. McClellan, J.H. |
| description | We estimate a vehicle's speed, its wheelbase length, and tire track length by jointly estimating its acoustic wave pattern with a single passive acoustic sensor that records the vehicle's drive-by noise. The acoustic wave pattern is determined using the vehicle's speed, the Doppler shift factor, the sensor's distance to the vehicle's closest-point-of-approach, and three envelope shape (ES) components, which approximate the shape variations of the received signal's power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM, the number of cylinders, and the vehicle's initial bearing, loudness and speed to form a vehicle profile vector. This vector provides a fingerprint that can be used for vehicle identification and classification. We also provide possible reasons why some of the existing methods are unable to provide unbiased vehicle speed estimates using the same framework. The approach is illustrated using vehicle speed estimation and classification results obtained with field data. |
| doi_str_mv | 10.1109/TSP.2008.2005750 |
| format | Article |
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The acoustic wave pattern is determined using the vehicle's speed, the Doppler shift factor, the sensor's distance to the vehicle's closest-point-of-approach, and three envelope shape (ES) components, which approximate the shape variations of the received signal's power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM, the number of cylinders, and the vehicle's initial bearing, loudness and speed to form a vehicle profile vector. This vector provides a fingerprint that can be used for vehicle identification and classification. We also provide possible reasons why some of the existing methods are unable to provide unbiased vehicle speed estimates using the same framework. The approach is illustrated using vehicle speed estimation and classification results obtained with field data.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2008.2005750</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Acoustic noise ; Acoustic sensors ; Acoustic signal analysis ; Acoustic waves ; Acoustics ; Applied sciences ; Classification ; Doppler shift ; Engine cylinders ; Envelopes ; Estimates ; estimation ; Exact sciences and technology ; Fingerprint recognition ; Information, signal and communications theory ; Mathematical analysis ; maximum-likelihood estimation ; Miscellaneous ; Noise shaping ; pattern classification ; Pattern recognition ; road vehicle identification ; Shape ; Signal and communications theory ; Signal processing ; Signal representation. Spectral analysis ; Signal, noise ; Telecommunications and information theory ; Tires ; Vectors (mathematics) ; Vehicle driving ; Vehicles</subject><ispartof>IEEE transactions on signal processing, 2009-01, Vol.57 (1), p.30-47</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c425t-9bd4ec131ced75dbf786163d37a065a38e30c55f5f6453cc7ebd364f1b1c935e3</citedby><cites>FETCH-LOGICAL-c425t-9bd4ec131ced75dbf786163d37a065a38e30c55f5f6453cc7ebd364f1b1c935e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4625947$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,4010,27900,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4625947$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21020779$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Cevher, V.</creatorcontrib><creatorcontrib>Chellappa, R.</creatorcontrib><creatorcontrib>McClellan, J.H.</creatorcontrib><title>Vehicle Speed Estimation Using Acoustic Wave Patterns</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>We estimate a vehicle's speed, its wheelbase length, and tire track length by jointly estimating its acoustic wave pattern with a single passive acoustic sensor that records the vehicle's drive-by noise. The acoustic wave pattern is determined using the vehicle's speed, the Doppler shift factor, the sensor's distance to the vehicle's closest-point-of-approach, and three envelope shape (ES) components, which approximate the shape variations of the received signal's power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM, the number of cylinders, and the vehicle's initial bearing, loudness and speed to form a vehicle profile vector. This vector provides a fingerprint that can be used for vehicle identification and classification. We also provide possible reasons why some of the existing methods are unable to provide unbiased vehicle speed estimates using the same framework. The approach is illustrated using vehicle speed estimation and classification results obtained with field data.</description><subject>Acoustic noise</subject><subject>Acoustic sensors</subject><subject>Acoustic signal analysis</subject><subject>Acoustic waves</subject><subject>Acoustics</subject><subject>Applied sciences</subject><subject>Classification</subject><subject>Doppler shift</subject><subject>Engine cylinders</subject><subject>Envelopes</subject><subject>Estimates</subject><subject>estimation</subject><subject>Exact sciences and technology</subject><subject>Fingerprint recognition</subject><subject>Information, signal and communications theory</subject><subject>Mathematical analysis</subject><subject>maximum-likelihood estimation</subject><subject>Miscellaneous</subject><subject>Noise shaping</subject><subject>pattern classification</subject><subject>Pattern recognition</subject><subject>road vehicle identification</subject><subject>Shape</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal representation. Spectral analysis</subject><subject>Signal, noise</subject><subject>Telecommunications and information theory</subject><subject>Tires</subject><subject>Vectors (mathematics)</subject><subject>Vehicle driving</subject><subject>Vehicles</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kM1LAzEQxYMoWKt3wcsiqKetk02y2RxLqR9QsNBWvYVsdla3bHdrshX8701p6cGDl0yY-c3w3iPkksKAUlD389l0kABk20dIAUekRxWnMXCZHoc_CBaLTL6fkjPvlwCUc5X2iHjFz8rWGM3WiEU09l21Ml3VNtHCV81HNLTtJvRs9Ga-MZqarkPX-HNyUpra48W-9sniYTwfPcWTl8fn0XASW56ILlZ5wdFSRi0WUhR5KbOUpqxg0kAqDMuQgRWiFGXKBbNWYl6wlJc0p1YxgaxP7nZ316792qDv9KryFuvaNBh06SwYTWSwEsjbf0nGleCMQgCv_4DLduOa4EIHcUFrRkWAYAdZ13rvsNRrF3JxP5qC3satQ9x6G7fexx1WbvZ3jbemLp1pbOUPewmFBKRUgbvacRUiHsY8TYTikv0C2omGfg</recordid><startdate>200901</startdate><enddate>200901</enddate><creator>Cevher, V.</creator><creator>Chellappa, R.</creator><creator>McClellan, J.H.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>200901</creationdate><title>Vehicle Speed Estimation Using Acoustic Wave Patterns</title><author>Cevher, V. ; Chellappa, R. ; McClellan, J.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c425t-9bd4ec131ced75dbf786163d37a065a38e30c55f5f6453cc7ebd364f1b1c935e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Acoustic noise</topic><topic>Acoustic sensors</topic><topic>Acoustic signal analysis</topic><topic>Acoustic waves</topic><topic>Acoustics</topic><topic>Applied sciences</topic><topic>Classification</topic><topic>Doppler shift</topic><topic>Engine cylinders</topic><topic>Envelopes</topic><topic>Estimates</topic><topic>estimation</topic><topic>Exact sciences and technology</topic><topic>Fingerprint recognition</topic><topic>Information, signal and communications theory</topic><topic>Mathematical analysis</topic><topic>maximum-likelihood estimation</topic><topic>Miscellaneous</topic><topic>Noise shaping</topic><topic>pattern classification</topic><topic>Pattern recognition</topic><topic>road vehicle identification</topic><topic>Shape</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal representation. Spectral analysis</topic><topic>Signal, noise</topic><topic>Telecommunications and information theory</topic><topic>Tires</topic><topic>Vectors (mathematics)</topic><topic>Vehicle driving</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cevher, V.</creatorcontrib><creatorcontrib>Chellappa, R.</creatorcontrib><creatorcontrib>McClellan, J.H.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Cevher, V.</au><au>Chellappa, R.</au><au>McClellan, J.H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Vehicle Speed Estimation Using Acoustic Wave Patterns</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2009-01</date><risdate>2009</risdate><volume>57</volume><issue>1</issue><spage>30</spage><epage>47</epage><pages>30-47</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>We estimate a vehicle's speed, its wheelbase length, and tire track length by jointly estimating its acoustic wave pattern with a single passive acoustic sensor that records the vehicle's drive-by noise. The acoustic wave pattern is determined using the vehicle's speed, the Doppler shift factor, the sensor's distance to the vehicle's closest-point-of-approach, and three envelope shape (ES) components, which approximate the shape variations of the received signal's power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM, the number of cylinders, and the vehicle's initial bearing, loudness and speed to form a vehicle profile vector. This vector provides a fingerprint that can be used for vehicle identification and classification. We also provide possible reasons why some of the existing methods are unable to provide unbiased vehicle speed estimates using the same framework. The approach is illustrated using vehicle speed estimation and classification results obtained with field data.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2008.2005750</doi><tpages>18</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Acoustic noise Acoustic sensors Acoustic signal analysis Acoustic waves Acoustics Applied sciences Classification Doppler shift Engine cylinders Envelopes Estimates estimation Exact sciences and technology Fingerprint recognition Information, signal and communications theory Mathematical analysis maximum-likelihood estimation Miscellaneous Noise shaping pattern classification Pattern recognition road vehicle identification Shape Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Tires Vectors (mathematics) Vehicle driving Vehicles |
| title | Vehicle Speed Estimation Using Acoustic Wave Patterns |
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