Modified Levenberg-Marquardt-Based Optimization Method for LiDAR Waveform Decomposition
A modified Levenberg-Marquardt (LM) method is proposed to improve the waveform-decomposition efficiency of light detection and ranging (LiDAR). The conventional constant-model-based LM fitting algorithm is subsequently modified using two proposed models: the linear model and exponential model. By re...
Gespeichert in:
| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2016-04, Vol.13 (4), p.530-534 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 534 |
|---|---|
| container_issue | 4 |
| container_start_page | 530 |
| container_title | IEEE geoscience and remote sensing letters |
| container_volume | 13 |
| creator | Xu, Fan Li, Fenfang Wang, Yuanqing |
| description | A modified Levenberg-Marquardt (LM) method is proposed to improve the waveform-decomposition efficiency of light detection and ranging (LiDAR). The conventional constant-model-based LM fitting algorithm is subsequently modified using two proposed models: the linear model and exponential model. By revising the update coefficient of the damping term to make it consistent with the variation of residual error, the magnitude of oscillation is effectively reduced to provide better convergence. The models were experimentally verified using observed data acquired by our experimental large-footprint LiDAR system. The results indicate that the two modified LM-based algorithms provide better performance in terms of convergence speed and iteration efficiency for waveform decomposition in comparison with the traditional algorithm. Most prominently, the exponential LM algorithm provides 69% maximum improvement in convergence speed and 103% in acceptable iteration efficiency in comparison with the traditional algorithm. |
| doi_str_mv | 10.1109/LGRS.2016.2522387 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_1816025645</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7416624</ieee_id><sourcerecordid>1816025645</sourcerecordid><originalsourceid>FETCH-LOGICAL-c326t-cd9fe0090dbc3dc20a4130bcb9ebf377ae99275250d32938abaa406406193d6e3</originalsourceid><addsrcrecordid>eNpdkEtLw0AQgBdRsFZ_gHgJePGSuo_s61jfQorgA70tm-xEtzTdupsK-utNaPEgDMwM880wfAgdEzwhBOvz8vbxaUIxERPKKWVK7qAR4VzlmEuyO9QFz7lWb_voIKU5xrRQSo7Q6yw433hwWQlfsKwgvuczGz_XNrouv7CpnzysOt_6H9v5sMxm0H0ElzUhZqW_mj5mr_YL-q7NrqAO7SokP3CHaK-xiwRH2zxGLzfXz5d3eflwe385LfOaUdHltdMNYKyxq2rmaoptQRiu6kpD1TApLWhNJaccO0Y1U7aytsCiD6KZE8DG6GxzdxXD5xpSZ1qfalgs7BLCOhmiiMCUi4L36Ok_dB7Wcdl_Z4hUElMlenNjRDZUHUNKERqzir618dsQbAbVZlBtBtVmq7rfOdnseAD442VBhKAF-wXYD3oP</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1787028622</pqid></control><display><type>article</type><title>Modified Levenberg-Marquardt-Based Optimization Method for LiDAR Waveform Decomposition</title><source>IEEE Electronic Library Online</source><creator>Xu, Fan ; Li, Fenfang ; Wang, Yuanqing</creator><creatorcontrib>Xu, Fan ; Li, Fenfang ; Wang, Yuanqing</creatorcontrib><description>A modified Levenberg-Marquardt (LM) method is proposed to improve the waveform-decomposition efficiency of light detection and ranging (LiDAR). The conventional constant-model-based LM fitting algorithm is subsequently modified using two proposed models: the linear model and exponential model. By revising the update coefficient of the damping term to make it consistent with the variation of residual error, the magnitude of oscillation is effectively reduced to provide better convergence. The models were experimentally verified using observed data acquired by our experimental large-footprint LiDAR system. The results indicate that the two modified LM-based algorithms provide better performance in terms of convergence speed and iteration efficiency for waveform decomposition in comparison with the traditional algorithm. Most prominently, the exponential LM algorithm provides 69% maximum improvement in convergence speed and 103% in acceptable iteration efficiency in comparison with the traditional algorithm.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2016.2522387</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Convergence ; Damping ; Decomposition ; Distance measurement ; Efficiency ; Exponential model ; Iterative methods ; Laser radar ; Lidar ; Light detection and ranging ; light detection and ranging (LiDAR) ; linear model ; modified Levenberg-Marquardt (LM) ; Noise reduction ; Smoothing methods ; waveform decomposition ; Waveforms</subject><ispartof>IEEE geoscience and remote sensing letters, 2016-04, Vol.13 (4), p.530-534</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c326t-cd9fe0090dbc3dc20a4130bcb9ebf377ae99275250d32938abaa406406193d6e3</citedby><cites>FETCH-LOGICAL-c326t-cd9fe0090dbc3dc20a4130bcb9ebf377ae99275250d32938abaa406406193d6e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7416624$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27929,27930,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7416624$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Xu, Fan</creatorcontrib><creatorcontrib>Li, Fenfang</creatorcontrib><creatorcontrib>Wang, Yuanqing</creatorcontrib><title>Modified Levenberg-Marquardt-Based Optimization Method for LiDAR Waveform Decomposition</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>A modified Levenberg-Marquardt (LM) method is proposed to improve the waveform-decomposition efficiency of light detection and ranging (LiDAR). The conventional constant-model-based LM fitting algorithm is subsequently modified using two proposed models: the linear model and exponential model. By revising the update coefficient of the damping term to make it consistent with the variation of residual error, the magnitude of oscillation is effectively reduced to provide better convergence. The models were experimentally verified using observed data acquired by our experimental large-footprint LiDAR system. The results indicate that the two modified LM-based algorithms provide better performance in terms of convergence speed and iteration efficiency for waveform decomposition in comparison with the traditional algorithm. Most prominently, the exponential LM algorithm provides 69% maximum improvement in convergence speed and 103% in acceptable iteration efficiency in comparison with the traditional algorithm.</description><subject>Algorithms</subject><subject>Convergence</subject><subject>Damping</subject><subject>Decomposition</subject><subject>Distance measurement</subject><subject>Efficiency</subject><subject>Exponential model</subject><subject>Iterative methods</subject><subject>Laser radar</subject><subject>Lidar</subject><subject>Light detection and ranging</subject><subject>light detection and ranging (LiDAR)</subject><subject>linear model</subject><subject>modified Levenberg-Marquardt (LM)</subject><subject>Noise reduction</subject><subject>Smoothing methods</subject><subject>waveform decomposition</subject><subject>Waveforms</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkEtLw0AQgBdRsFZ_gHgJePGSuo_s61jfQorgA70tm-xEtzTdupsK-utNaPEgDMwM880wfAgdEzwhBOvz8vbxaUIxERPKKWVK7qAR4VzlmEuyO9QFz7lWb_voIKU5xrRQSo7Q6yw433hwWQlfsKwgvuczGz_XNrouv7CpnzysOt_6H9v5sMxm0H0ElzUhZqW_mj5mr_YL-q7NrqAO7SokP3CHaK-xiwRH2zxGLzfXz5d3eflwe385LfOaUdHltdMNYKyxq2rmaoptQRiu6kpD1TApLWhNJaccO0Y1U7aytsCiD6KZE8DG6GxzdxXD5xpSZ1qfalgs7BLCOhmiiMCUi4L36Ok_dB7Wcdl_Z4hUElMlenNjRDZUHUNKERqzir618dsQbAbVZlBtBtVmq7rfOdnseAD442VBhKAF-wXYD3oP</recordid><startdate>201604</startdate><enddate>201604</enddate><creator>Xu, Fan</creator><creator>Li, Fenfang</creator><creator>Wang, Yuanqing</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope></search><sort><creationdate>201604</creationdate><title>Modified Levenberg-Marquardt-Based Optimization Method for LiDAR Waveform Decomposition</title><author>Xu, Fan ; Li, Fenfang ; Wang, Yuanqing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c326t-cd9fe0090dbc3dc20a4130bcb9ebf377ae99275250d32938abaa406406193d6e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Convergence</topic><topic>Damping</topic><topic>Decomposition</topic><topic>Distance measurement</topic><topic>Efficiency</topic><topic>Exponential model</topic><topic>Iterative methods</topic><topic>Laser radar</topic><topic>Lidar</topic><topic>Light detection and ranging</topic><topic>light detection and ranging (LiDAR)</topic><topic>linear model</topic><topic>modified Levenberg-Marquardt (LM)</topic><topic>Noise reduction</topic><topic>Smoothing methods</topic><topic>waveform decomposition</topic><topic>Waveforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Fan</creatorcontrib><creatorcontrib>Li, Fenfang</creatorcontrib><creatorcontrib>Wang, Yuanqing</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) Online</collection><collection>IEEE Electronic Library Online</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</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><jtitle>IEEE geoscience and remote sensing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xu, Fan</au><au>Li, Fenfang</au><au>Wang, Yuanqing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modified Levenberg-Marquardt-Based Optimization Method for LiDAR Waveform Decomposition</atitle><jtitle>IEEE geoscience and remote sensing letters</jtitle><stitle>LGRS</stitle><date>2016-04</date><risdate>2016</risdate><volume>13</volume><issue>4</issue><spage>530</spage><epage>534</epage><pages>530-534</pages><issn>1545-598X</issn><eissn>1558-0571</eissn><coden>IGRSBY</coden><abstract>A modified Levenberg-Marquardt (LM) method is proposed to improve the waveform-decomposition efficiency of light detection and ranging (LiDAR). The conventional constant-model-based LM fitting algorithm is subsequently modified using two proposed models: the linear model and exponential model. By revising the update coefficient of the damping term to make it consistent with the variation of residual error, the magnitude of oscillation is effectively reduced to provide better convergence. The models were experimentally verified using observed data acquired by our experimental large-footprint LiDAR system. The results indicate that the two modified LM-based algorithms provide better performance in terms of convergence speed and iteration efficiency for waveform decomposition in comparison with the traditional algorithm. Most prominently, the exponential LM algorithm provides 69% maximum improvement in convergence speed and 103% in acceptable iteration efficiency in comparison with the traditional algorithm.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LGRS.2016.2522387</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1545-598X |
| ispartof | IEEE geoscience and remote sensing letters, 2016-04, Vol.13 (4), p.530-534 |
| issn | 1545-598X 1558-0571 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1816025645 |
| source | IEEE Electronic Library Online |
| subjects | Algorithms Convergence Damping Decomposition Distance measurement Efficiency Exponential model Iterative methods Laser radar Lidar Light detection and ranging light detection and ranging (LiDAR) linear model modified Levenberg-Marquardt (LM) Noise reduction Smoothing methods waveform decomposition Waveforms |
| title | Modified Levenberg-Marquardt-Based Optimization Method for LiDAR Waveform Decomposition |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-13T21%3A47%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Modified%20Levenberg-Marquardt-Based%20Optimization%20Method%20for%20LiDAR%20Waveform%20Decomposition&rft.jtitle=IEEE%20geoscience%20and%20remote%20sensing%20letters&rft.au=Xu,%20Fan&rft.date=2016-04&rft.volume=13&rft.issue=4&rft.spage=530&rft.epage=534&rft.pages=530-534&rft.issn=1545-598X&rft.eissn=1558-0571&rft.coden=IGRSBY&rft_id=info:doi/10.1109/LGRS.2016.2522387&rft_dat=%3Cproquest_RIE%3E1816025645%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1787028622&rft_id=info:pmid/&rft_ieee_id=7416624&rfr_iscdi=true |