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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50055

Title: Designing Fuzzy Adaptive Nonlinear Filter for Land Vehicle Ultra-Tightly Coupled Integrated Navigation Sensor Fusion
Authors: Chien-Hao Tseng
Dah-Jing Jwo
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Keywords: Fuzzy logic
Unscented Kalman filter
Ultra-tightly coupled
Integrated navigation
Date: 2011-05
Issue Date: 2018-09-14T01:05:24Z
Publisher: Sensors & Transducers Journal
Abstract: Abstract: Traditional GPS/INS integration designs adopt a loosely or tightly coupled architecture, for which the GPS receiver may lose lock due to the interference/jamming scenarios and high dynamic environments. This paper presents a sensor fusion method based on the combination of unscented Kalman filter (UKF) and Fuzzy Logic Adaptive System (FLAS) for the ultra-tightly coupled GPS/INS integrated navigation. An ultra-tight GPS/INS architecture involves the integration of I and Q (in-phase and quadrature) components from the correlator of a GPS receiver with the INS data. The UKF employs a set of sigma points through deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. The fuzzy logic adaptive system (FLAS) has been one of the approaches to prevent divergence problem of the filter when precise knowledge on the system models are not available. Though the use of fuzzy inference system (FIS), the FLAS has been incorporated into the UKF as a mechanism for timely detecting the dynamical changes and implementing the on-line tuning of the process noise covariance by monitoring the innovation information, and therefore improves the estimation performance. The results show that the proposed fuzzy adaptive UKF algorithm can effectively improve the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and EKF.

(PDF) Designing Fuzzy Adaptive Nonlinear Filter for Land Vehicle Ultra-Tightly Coupled Integrated Navigation Sensor Fusion. Available from: https://www.researchgate.net/publication/290938807_Designing_Fuzzy_Adaptive_Nonlinear_Filter_for_Land_Vehicle_Ultra-Tightly_Coupled_Integrated_Navigation_Sensor_Fusion [accessed Sep 14 2018].
Relation: 128(5)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50055
Appears in Collections:[通訊與導航工程學系] 期刊論文

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