Strong Tracking Kalman Filter Research Articles

Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages

Aiming to improve positioning precision of the GPS/INS integrated navigation system during GPS outages, a novel model combined with strong tracking Kalman filter (STKF) and wavelet neural network (WNN) algorithms for INS errors compensation is proposed and tested. STKF is used to estimate INS errors as a replacement of Kalman filter (KF), and WNN is applied to establish a highly accurate model based on STKF when GPS works well and to predict INS errors during GPS outages. Performance of the proposed model has been experimentally verified using GPS and INS data collected in a land vehicle navigation test. The comparison results indicate that the proposed model combined with STKF/WNN algorithms can effectively provide high accurate corrections to the standalone INS during GPS outages.

Adaptive Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation

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

An integrated GPS/DR navigation system for AUV

GPS/Dead-reckoning navigation system for autonomous underwater vehicle (AUV) is introduced, which includes navigation overall architecture, hardware and software structure. Dead-reckoning theory is presented in details. And the strong tracking Kalman filter and Singer model are applied to handle the imprecise navigation mode, which can improve the navigation system’s precision and reliability. Finally, the sea experiments which include autonomous search mission in an unknown area and long distance motion are conducted to demonstrate the reliability and feasibility of the navigation system.