Adaptive Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation

Abstract

Abstract-The well-known extended Kalman filter (EKF) has been widely applied to the Global Positioning System (GPS) navigation processing. The adaptive algorithm has been one of the approaches to prevent the divergence problem of the EKF when precise knowledge on the system models are not available. One of the adaptive methods is called the strong tracking Kalman filter (STKF), which is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. Traditional approach for selecting the softening factors heavily relies on personal experience or computer simulation. In order to resolve this shortcoming, a novel scheme called the adaptive fuzzy strong tracking Kalman filter (AFSTKF) is carried out. In the AFSTKF, the fuzzy logic reasoning system based on the Takagi-Sugeno (T-S) model is incorporated into the STKF. By monitoring the degree of divergence (DOD) parameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the softening factor according to the change in vehicle dynamics. GPS navigation processing using the AFSTKF will be simulated to validate the effectiveness of the proposed strategy. The performance of the proposed scheme will be assessed and compared with those of conventional EKF and STKF.Index Terms-Adaptive extended Kalman filtering, fuzzy logic adaptive system (FLAS), global positioning system (GPS), strong tracking Kalman filter (STKF).

Review of Hybridizations of Kalman Filters with Fuzzy and Neural Computing for Mobile Robot Navigation

“…A suboptimal algorithm to set the filter coefficients has been called the Strong Tracking Kalman Filter (STKF). In [10] an adaptive method to set the STKF parameters has been proposed, based on a Takagi-Sugeno fuzzy system defined on the innovation (the difference between the predicted and observed sensor information) divergence. However, the rules of the Takagi-Sugeno are built beforehand, with membership functions defined arbitrarily.…”

Fuzzy logic-based multi-factor aided multiple-model filter for general aviation target tracking

“…Since target recognition usually outputs the possibility of target types, the output is fuzzy in nature. Furthermore, meteorology (such as airflow) influence the flight in a complicated way, which can be modeled by fuzzy relations [2,5,7]. Considering of the above situations, a novel fuzzy logic-based multi-factor aided multiple-model filter (FLMAMMF) is proposed which employs the above influencing factors to adjust TPM.…”

Target Tracking Based on Amendatory Sage-Husa Adaptive Kalman Filtering

“…Such as adaptive fading Kalman filter based on innovation covariance [4] , adaptive Kalman filter based on neural network and based on fuzzy logic [5] [6] , adaptive algorithm for adjusting observation noises based on double-Kalman filter [7] , methods of adaptive filter to integrated navigation system of autonomous underwater vehicle [8] , etc.In the mentioned literature [8] , Sage -Husa adaptive Kalman filter is most widely applied. But, when the state of motion mutates, the model takes a long time to reach a steady state, and the accuracy of model is low.…”