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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50053

Title: A New Optimized Algorithm with Nonlinear Filter for Ultra-Tightly Coupled Integrated Navigation System of Land Vehicle
Authors: Chien-Hao Tseng
Dah-Jing Jwo
Chin-Wen Chang
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Keywords: Ultra-tightly coupled
Fuzzy logic
Extended Kalman filter (EKF)
Unscented Kalman filter (UKF)
Extended particle filter (EPF)
Date: 2012-01
Issue Date: 2018-09-14
Publisher: CMC-Computers Materials & Continua
Abstract: Abstract: The extended particle filter (EPF) assisted by the Takagi-Sugeno (TS)
fuzzy logic adaptive system (FLAS) is used to design the ultra-tightly coupled
GPS/INS (inertial navigation system) integrated navigation, which can maneuver
the vehicle environment and the GPS outages scenario. The traditional integrated
navigation designs adopt a loosely or tightly coupled architecture, for which the
GPS receiver may lose the lock due to the interference/jamming scenarios, high
dynamic environments, and the periods of partial GPS shading. An ultra-tight
GPS/INS architecture involves the integration of I (in-phase) and Q (quadrature)
components from the correlator of a GPS receiver with the INS data. The EPF
is a particle filter (PF) which uses the extended Kalman filter (EKF) to generate
the proposal distribution. The PF depends mostly on the number of particles in
order to achieve a better performance during the high dynamic environments and
GPS outages. The T-S FLAS is one of these approaches that can prevent the divergence
problem of the filter when the precise knowledge on the system models is not
available. The results show that the proposed fuzzy adaptive EPF (FAEPF) can effectively
improve the navigation estimation accuracy and reduce the computational
load as compared with the EPF and the unscented Kalman filter (UKF).
Relation: 27(1) pp.23-53
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50053
Appears in Collections:[通訊與導航工程學系] 期刊論文

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