A New Outlier-Robust Student's t Based Gaussian Approximate Filter for Cooperative Localization
In this paper, a new outlier-robust Student's t based Gaussian approximate filter is proposed to address the heavy-tailed process and measurement noises induced by the outlier measurements of velocity and range in cooperative localization of autonomous underwater vehicles (AUVs). The state vect...
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| Veröffentlicht in: | IEEE/ASME transactions on mechatronics 2017-10, Vol.22 (5), p.2380-2386 |
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| container_title | IEEE/ASME transactions on mechatronics |
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| creator | Yulong Huang Yonggang Zhang Bo Xu Zhemin Wu Chambers, Jonathon |
| description | In this paper, a new outlier-robust Student's t based Gaussian approximate filter is proposed to address the heavy-tailed process and measurement noises induced by the outlier measurements of velocity and range in cooperative localization of autonomous underwater vehicles (AUVs). The state vector, scale matrices, and degrees of freedom (DOF) parameters are jointly estimated based on the variational Bayesian approach by using the constructed Student's t based hierarchical Gaussian state-space model. The performances of the proposed filter and existing filters are tested in the cooperative localization of an AUV through a lake trial. Experimental results illustrate that the proposed filter has better localization accuracy and robustness than existing state-of-the-art outlier-robust filters. |
| doi_str_mv | 10.1109/TMECH.2017.2744651 |
| format | Article |
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The state vector, scale matrices, and degrees of freedom (DOF) parameters are jointly estimated based on the variational Bayesian approach by using the constructed Student's t based hierarchical Gaussian state-space model. The performances of the proposed filter and existing filters are tested in the cooperative localization of an AUV through a lake trial. 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(IEEE) 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c339t-d5be1a0facd7183507b517c5e5611741cab09d1ba07ed733b12b12d67f7771393</citedby><cites>FETCH-LOGICAL-c339t-d5be1a0facd7183507b517c5e5611741cab09d1ba07ed733b12b12d67f7771393</cites><orcidid>0000-0003-4548-1111</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8016598$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8016598$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yulong Huang</creatorcontrib><creatorcontrib>Yonggang Zhang</creatorcontrib><creatorcontrib>Bo Xu</creatorcontrib><creatorcontrib>Zhemin Wu</creatorcontrib><creatorcontrib>Chambers, Jonathon</creatorcontrib><title>A New Outlier-Robust Student's t Based Gaussian Approximate Filter for Cooperative Localization</title><title>IEEE/ASME transactions on mechatronics</title><addtitle>TMECH</addtitle><description>In this paper, a new outlier-robust Student's t based Gaussian approximate filter is proposed to address the heavy-tailed process and measurement noises induced by the outlier measurements of velocity and range in cooperative localization of autonomous underwater vehicles (AUVs). 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Experimental results illustrate that the proposed filter has better localization accuracy and robustness than existing state-of-the-art outlier-robust filters.</description><subject>Acoustic measurements</subject><subject>Autonomous underwater vehicles</subject><subject>Autonomous underwater vehicles (AUVs)</subject><subject>Bayes methods</subject><subject>Bayesian analysis</subject><subject>cooperative localization</subject><subject>Covariance matrices</subject><subject>Degrees of freedom</subject><subject>Gaussian process</subject><subject>heavy-tailed noise</subject><subject>Localization</subject><subject>Matrix algebra</subject><subject>Matrix methods</subject><subject>Noise measurement</subject><subject>nonlinear filtering</subject><subject>outlier</subject><subject>Parameter estimation</subject><subject>Robustness</subject><subject>State space models</subject><subject>State-space methods</subject><subject><![CDATA[Student's <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> t</tex-math> </inline-formula> </named-content> distribution]]></subject><subject>variational Bayesian</subject><subject>Velocity measurement</subject><issn>1083-4435</issn><issn>1941-014X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kNtKxDAQhosouK6-gN4EvPCqa6ZJmvZyLXsQVhd0Be9C2k6hS21qknp6ervuIgzMDPz_HL4guAQ6AaDp7eZhli0nEQU5iSTnsYCjYAQph5ACfz0eapqwkHMmToMz57aUUg4URoGakkf8JOveNzXa8MnkvfPk2fcltv7GEU_utMOSLHTvXK1bMu06a77qN-2RzOvGoyWVsSQzpkOrff2BZGUK3dQ_Q2Pa8-Ck0o3Di0MeBy_z2SZbhqv14j6brsKCsdSHpcgRNK10UUpImKAyFyALgSIGkBwKndO0hFxTiaVkLIdoiDKWlZQSWMrGwfV-7nDde4_Oq63pbTusVJAKAJ7G0U4V7VWFNc5ZrFRnh1fstwKqdiDVH0i1A6kOIAfT1d5UI-K_IaEQizRhv-6ibvs</recordid><startdate>201710</startdate><enddate>201710</enddate><creator>Yulong Huang</creator><creator>Yonggang Zhang</creator><creator>Bo Xu</creator><creator>Zhemin Wu</creator><creator>Chambers, Jonathon</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The state vector, scale matrices, and degrees of freedom (DOF) parameters are jointly estimated based on the variational Bayesian approach by using the constructed Student's t based hierarchical Gaussian state-space model. The performances of the proposed filter and existing filters are tested in the cooperative localization of an AUV through a lake trial. Experimental results illustrate that the proposed filter has better localization accuracy and robustness than existing state-of-the-art outlier-robust filters.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TMECH.2017.2744651</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0003-4548-1111</orcidid><oa>free_for_read</oa></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Acoustic measurements Autonomous underwater vehicles Autonomous underwater vehicles (AUVs) Bayes methods Bayesian analysis cooperative localization Covariance matrices Degrees of freedom Gaussian process heavy-tailed noise Localization Matrix algebra Matrix methods Noise measurement nonlinear filtering outlier Parameter estimation Robustness State space models State-space methods Student's <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> t</tex-math> </inline-formula> </named-content> distribution variational Bayesian Velocity measurement |
| title | A New Outlier-Robust Student's t Based Gaussian Approximate Filter for Cooperative Localization |
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