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Vol. 128, Issue 5, May 2011, pp.1-16




Designing Fuzzy Adaptive Nonlinear Filter for Land Vehicle Ultra-Tightly Coupled Integrated Navigation Sensor Fusion

Chien-Hao TSENG, Dah-Jing JWO

Applied Scientific Computing Division

National Center for High-Performance Computing

No. 22, Keyuan Rd., Central Taiwan Science Park, Taichung 407-63, Taiwan

Tel.: +886-4-24620202 ext. 855, fax: +886-4-24627373

E-mail: c00how00@nchc.narl.org.tw



Received: 5 May 2011   /Accepted: 24 May 2011   /Published: 28 May 2011


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.


Keywords: Fuzzy logic, Unscented Kalman filter, Ultra-tightly coupled, Integrated navigation


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