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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50051

Title: Nonlinear Filtering with IMM Algorithm for Ultra-Tight GPS/INS Integration
Authors: Dah-Jing Jwo
Chia-Wei Hu
Chien-Hao Tseng
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Keywords: GPS
Ultra-Tight Integration
Interacting Multiple Model
Unscented Kalman Filter
Date: 2013-05
Issue Date: 2018-09-13T07:55:16Z
Publisher: International Journal of Advanced Robotic Systems
Abstract: Abstract: Abstract This paper conducts a performance evaluation for the ultra-tight integration of a Global positioning system (GPS) and an inertial navigation system (INS), using nonlinear filtering approaches with an interacting multiple model (IMM) algorithm. An ultra-tight GPS/INS architecture involves the integration of in-phase and quadrature components from the correlator of a GPS receiver with INS data. An unscented Kalman filter (UKF), which employs a set of sigma points by deterministic sampling, avoids the error caused by linearization as in an extended Kalman filter (EKF). Based on the filter structural adaptation for describing various dynamic behaviours, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the ultra-tight GPS/INS integration. The use of IMM enables tuning of an appropriate value for the process of noise covariance so as to maintain good estimation accuracy and tracking capability. Two examples are provided to illustrate the effectiveness of the design and demonstrate the effective improvement in navigation estimation accuracy. A performance comparison among various filtering methods for ultra-tight integration of GPS and INS is also presented. The IMM based nonlinear filtering approach demonstrates the effectiveness of the algorithm for improved positioning performance.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50051
Appears in Collections:[通訊與導航工程學系] 期刊論文

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