http://scholars.ntou.edu.tw/handle/123456789/4865
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dah-Jing Jwo | en_US |
dc.contributor.author | Lai, C. N. | en_US |
dc.date.accessioned | 2020-11-19T03:03:42Z | - |
dc.date.available | 2020-11-19T03:03:42Z | - |
dc.date.issued | 2008-09 | - |
dc.identifier.issn | 1080-5370 | - |
dc.identifier.uri | http://scholars.ntou.edu.tw/handle/123456789/4865 | - |
dc.description.abstract | This paper preliminarily investigates the application of unscented Kalman filter (UKF) approach with nonlinear dynamic process modeling for Global positioning system (GPS) navigation processing. Many estimation problems, including the GPS navigation, are actually nonlinear. Although it has been common that additional fictitious process noise can be added to the system model, however, the more suitable cure for non convergence caused by unmodeled states is to correct the model. For the nonlinear estimation problem, alternatives for the classical model-based extended Kalman filter (EKF) can be employed. The UKF is a nonlinear distribution approximation method, which uses a finite number of sigma points to propagate the probability of state distribution through the nonlinear dynamics of system. The UKF exhibits superior performance when compared with EKF since the series approximations in the EKF algorithm can lead to poor representations of the nonlinear functions and probability distributions of interest. GPS navigation processing using the proposed approach will be conducted to validate the effectiveness of the proposed strategy. The performance of the UKF with nonlinear dynamic process model will be assessed and compared to those of conventional EKF. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Gps Solutions | en_US |
dc.subject | Extended Kalman filterl | en_US |
dc.subject | Unscented Kalman filter | en_US |
dc.subject | Nonlinear model Globa | en_US |
dc.subject | positioning system (GPS) | en_US |
dc.title | Unscented Kalman filter with nonlinear dynamic process modeling for GPS navigation | en_US |
dc.type | journal article | en_US |
dc.identifier.doi | <Go to ISI>://WOS:000258529900003 | - |
dc.identifier.doi | <Go to ISI>://WOS:000258529900003 | - |
dc.identifier.doi | 10.1007/s10291-007-0081-9 | - |
dc.identifier.doi | <Go to ISI>://WOS:000258529900003 | - |
dc.identifier.doi | <Go to ISI>://WOS:000258529900003 | - |
dc.identifier.url | <Go to ISI>://WOS:000258529900003 | |
dc.relation.journalvolume | 12 | en_US |
dc.relation.journalissue | 4 | en_US |
dc.relation.pages | pages249–260 | en_US |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.openairetype | journal article | - |
item.grantfulltext | none | - |
item.languageiso639-1 | en | - |
item.fulltext | no fulltext | - |
crisitem.author.dept | College of Electrical Engineering and Computer Science | - |
crisitem.author.dept | Department of Communications, Navigation and Control Engineering | - |
crisitem.author.dept | National Taiwan Ocean University,NTOU | - |
crisitem.author.parentorg | National Taiwan Ocean University,NTOU | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
Appears in Collections: | 通訊與導航工程學系 |
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