The Fractional Kalman Filter-Based Asynchronous Multirate Sensor Information Fusion

Guangyue Xue,Wei Zhao,Jing Guo,Yubin Xu

doi:10.1155/2018/1450353

Abstract

A fractional Kalman filter-based multirate sensor fusion algorithm is presented to fuse the asynchronous measurements of the multirate sensors. Based on the characteristics of multirate and delay measurement, the state is reestimated at the time when the delayed measurement occurs by using weighted fractional Kalman filter, and then the state estimation is updated at the current time when the delayed measurement arrives following the similar pattern of Kalman filter. The simulation examples are given to illustrate the effectiveness of the proposed fusion method.

Highlights

Multisensor information fusion has been a key issue in sensor research since the 1970s and it has been applied in many fields, such as navigation, tracking, control, and wireless sensor networks
Fractional-order filters are used in some fields, the fractional-order-based asynchronous multirate sensor fusion is not considered
We present a novel fractional fusion algorithm to the asynchronous multirate sensor systems

Summary

Introduction

Multisensor information fusion has been a key issue in sensor research since the 1970s and it has been applied in many fields, such as navigation, tracking, control, and wireless sensor networks. In [3], the fractional-order Kalman filter was introduced to fuse the MEMS (microelectromechanical systems) sensor data, which was successfully applied in the estimation of motion problems. In [4], the extended fractional Kalman filter is utilized for state estimation strategy for fractional-order systems with noises and multiple time delayed measurements. Fractional-order filters are used in some fields, the fractional-order-based asynchronous multirate sensor fusion is not considered. In [13], the asynchronous multirate information fusion was modeled, and Kalman filter-based information fusion algorithm was proposed. We present a novel fractional fusion algorithm to the asynchronous multirate sensor systems. The fractional multirate sensor system is addressed, and the fractional Kalman filter is used for asynchronous fusion algorithm, such that the fusion results achieve high-precision and economic storage space

Problem Formulations

Fractional Kalman Filter

Asynchronous Multirate Sensor Information Fusion

The Stability Analysis of Fractional Kalman Filter

Simulation Results

Conclusion