An adaptive filtering method based on EMD for X-ray pulsar navigation with uncertain measurement noise

N Li,X M Xu,J Liu,Z W Kang,P Liu,X Tu

doi:10.1051/matecconf/201711404017

Abstract

Affected by the unstable pulse radiation and the pulsar directional errors, the statistical characteristics of the pulsar measurement noise may vary with time slowly and cannot be accurately determined, which cause the filtering accuracy of the extended Kalman filter(EKF) in pulsar navigation positioning system decline sharply or even diverge. To solve this problem, an adaptive extended Kalman filtering algorithm based on the empirical mode decomposition(EMD) is proposed. In this method, the high frequency noise is separated from measurement information of pulsar by the method of EMD, and the noise variance can be estimated to update the parameters of EKF. The simulation results demonstrate that compared with conventional EKF, the proposed method can adaptively track the change of the measurement noise, and still keeps high estimation accuracy with unknown measurement noise, the positioning accuracy of the pulsar navigation is improved simultaneously.

Highlights

X-ray pulsar navigation is an emerging autonomous navigation technology for spacecraft, which can provide high precision navigation information[1,2,3]
Affected by the unstable pulse radiation, the pulsar background noise and the pulsar directional errors, the statistical characteristics of the pulsar measurement noise may vary with time slowly and cannot be accurately determined limited to the current measurement equipment [11,12]
Many scholars have proposed a variety of adaptive Kalman filtering algorithm which mainly concentrated in two aspects: adaptive filtering based on innovation and multiple model

Summary

Introduction

X-ray pulsar navigation is an emerging autonomous navigation technology for spacecraft, which can provide high precision navigation information[1,2,3]. A At present, the EKF is the most widely used filtering method in the study of autonomous navigation of spacecraft based on X-ray pulsars[8,9]. Scince EKF can not adjust to the change of the measurement noise, the filtering accuracy of EKF will decline or even diverge. To this end, many scholars have proposed a variety of adaptive Kalman filtering algorithm which mainly concentrated in two aspects: adaptive filtering based on innovation and multiple model. Estimation(MME)[13,14,15,16,17] The former can adjust the covariance matrix of measurement noise by introducing the adjustment factor, and the calculation is simple but the filtering accuracy is limited. The noise variance is estimated and the relevant parameter of the EKF algorithm is modified in real time to realize the state estimation of the spacecraft

The obit dynamic model

The measurement model of X-ray pulsar with uncertain noise

Adaptive filtering algorithm based on empirical mode decomposition

The empirical mode decomposition

Simulation results and analysis

Conclusion