Radial basis function network-based available measurement classification of interferometric radar altimeter for terrain-aided navigation

Cited 7 time in webofscience Cited 0 time in scopus
  • Hit : 272
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorLee, Jungshinko
dc.contributor.authorBang, Hyochoongko
dc.date.accessioned2018-09-18T06:36:09Z-
dc.date.available2018-09-18T06:36:09Z-
dc.date.created2018-09-10-
dc.date.created2018-09-10-
dc.date.issued2018-09-
dc.identifier.citationIET RADAR SONAR AND NAVIGATION, v.12, no.9, pp.920 - 930-
dc.identifier.issn1751-8784-
dc.identifier.urihttp://hdl.handle.net/10203/245647-
dc.description.abstractThe purpose of this study is to propose a measurement classification method necessary to implement precision terrain-aided navigation (TAN) by using an interferometric radar altimeter (IRA) as a technology that can replace global positioning system/inertial navigation system integrated navigation. IRA is a sensor that extracts the angle perpendicular to the direction of flight, the look angle, and the slant range from the aircraft to the nearest terrain point. Unlike the radio altimeter which only measures the direct downward distance, IRA can be converted into three-dimensional coordinates in the navigation system. However, the IRA output has a disadvantage that it has uncertainty that cannot be predicted due to the signal processing and environmental factors. Therefore, a useful navigation technique for classifying sensor outputs is needed to implement precision TAN. This study introduces the radial basis function network and extreme learning machine methods to classify available IRA measurements and verifies the suitability of the proposed classification method by applying it to the bank of Kalman filter) and particle filter-based TAN.-
dc.languageEnglish-
dc.publisherINST ENGINEERING TECHNOLOGY-IET-
dc.subjectPARTICLE FILTER-
dc.subjectALGORITHM-
dc.titleRadial basis function network-based available measurement classification of interferometric radar altimeter for terrain-aided navigation-
dc.typeArticle-
dc.identifier.wosid000442375700002-
dc.identifier.scopusid2-s2.0-85052286180-
dc.type.rimsART-
dc.citation.volume12-
dc.citation.issue9-
dc.citation.beginningpage920-
dc.citation.endingpage930-
dc.citation.publicationnameIET RADAR SONAR AND NAVIGATION-
dc.identifier.doi10.1049/iet-rsn.2018.0079-
dc.contributor.localauthorBang, Hyochoong-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorradar interferometry-
dc.subject.keywordAuthorradar signal processing-
dc.subject.keywordAuthorsignal classification-
dc.subject.keywordAuthorGlobal Positioning System-
dc.subject.keywordAuthorradial basis function networks-
dc.subject.keywordAuthorinertial navigation-
dc.subject.keywordAuthorsensors-
dc.subject.keywordAuthorneural nets-
dc.subject.keywordAuthorlearning (artificial intelligence)-
dc.subject.keywordAuthorradar altimetry-
dc.subject.keywordAuthorradar computing-
dc.subject.keywordAuthorradial basis function network-
dc.subject.keywordAuthorinterferometric radar altimeter-
dc.subject.keywordAuthorterrain-aided navigation-
dc.subject.keywordAuthormeasurement classification method-
dc.subject.keywordAuthorGlobal Positioning System-
dc.subject.keywordAuthorinertial navigation system-
dc.subject.keywordAuthorangle extraction-
dc.subject.keywordAuthornearest terrain point-
dc.subject.keywordAuthorradio altimeter-
dc.subject.keywordAuthordirect downward distance measurement-
dc.subject.keywordAuthorthree-dimensional coordinates-
dc.subject.keywordAuthorsignal processing-
dc.subject.keywordAuthorsensor output classification-
dc.subject.keywordAuthorextreme learning machine methods-
dc.subject.keywordAuthorIRA measurements-
dc.subject.keywordAuthorparticle filter-based TAN-
dc.subject.keywordPlusPARTICLE FILTER-
dc.subject.keywordPlusALGORITHM-
Appears in Collection
AE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 7 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0