An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems

Rene Jr. Landry,Daidai Chen,Dongxue Guan,Jianhua Cheng,Lin Zhao

doi:10.1155/2014/451939

Abstract

MEMS/GPS integrated navigation system has been widely used for land-vehicle navigation. This system exhibits large errors because of its nonlinear model and uncertain noise statistic characteristics. Based on the principles of the adaptive Kalman filtering (AKF) and unscented Kalman filtering (AUKF) algorithms, an adaptive unscented Kalman filtering (AUKF) algorithm is proposed. By using noise statistic estimator, the uncertain noise characteristics could be online estimated to adaptively compensate the time-varying noise characteristics. Employing the adaptive filtering principle into UKF, the nonlinearity of system can be restrained. Simulations are conducted for MEMS/GPS integrated navigation system. The results show that the performance of estimation is improved by the AUKF approach compared with both conventional AKF and UKF.

Highlights

The results show that the performance of estimation is improved by the AUKF approach compared with both conventional adaptive Kalman filtering (AKF) and UKF
Microelectromechanical systems (MEMS) and Global Positioning System (GPS) integrated navigation system have the advantages of small size, light weight, and low cost, but, because of its low accuracy, it can only be applied in low accuracy navigation fields such as unmanned aircrafts and land-vehicles [1, 2]
The performance of AUKF applied in land navigation is evaluated by simulations and the results show that the integrated system exhibits excellent robustness and navigation performance

Summary

Introduction

Microelectromechanical systems (MEMS) and Global Positioning System (GPS) integrated navigation system have the advantages of small size, light weight, and low cost, but, because of its low accuracy, it can only be applied in low accuracy navigation fields such as unmanned aircrafts and land-vehicles [1, 2]. By utilizing the innovation and residual information, the AKF could adapt the filter stochastic properties online to accommodate itself to changes in vehicle dynamics. This technique could reduce the reliance of filter on the prior statistical information and obtain the noise statistic parameters of the dynamic system. As a combination of AKF and UKF, the adaptive UKF has been developed and applied to nonlinear joint estimation of both time-varying states and parameters [11]. The adaptive principles have been employed to update the means and covariances of the process and measurement noises online. A new AUKF algorithm is proposed for MEMS/GPS integrated navigation systems in vehicle applications. The performance of AUKF applied in land navigation is evaluated by simulations and the results show that the integrated system exhibits excellent robustness and navigation performance

Unscented Kalman Filtering Algorithm for Nonzero Mean Noise

Noise Statistic Estimator

Noise Statistic Estimator for UKF

Recursive Equations of Adaptive Unscented Kalman Filtering Algorithm

Findings

Conclusions