Abstract
In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors 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. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.
Highlights
A navigation filter is commonly designed by use of an extended Kalman filter (EKF) [1,2,3] to estimate the vehicle state variables and suppress the navigation measurement noise
The resulting IMMUKF ensures better description on the vehicle dynamics and exhibits superior navigation accuracy when compared with the classical unscented Kalman filter (UKF) algorithm
This paper has presented a fuzzy interacting multiple model unscented Kalman filter for Global Positioning System (GPS)/inertial navigation system (INS) navigation processing to prevent the divergence problem in high dynamic environments
Summary
A navigation filter is commonly designed by use of an extended Kalman filter (EKF) [1,2,3] to estimate the vehicle state variables and suppress the navigation measurement noise. The objective is to design the nonlinear filter in an IMM algorithm suitable for high dynamic or curvilinear motions to navigate a maneuvering vehicle. To deal with the noise uncertainty and system nonlinearity simultaneously, the IMMUKF can be introduced [24,25] In the approach, these multiple models are developed to describe various dynamic behaviors. By monitoring the innovation information, the Fuzzy logic adaptive system (FLAS) is employed for dynamically on-line determining better lower and upper bounds of the process noise covariance according to the innovation information, and improves the estimation performance.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
