A MIMU/GPS/magnetometer integrated alignment method based on sequential robust estimation

Shida Wang,Haobo Li,Jian Wang,Hang Yu

doi:10.1186/s41445-018-0019-2

Abstract

For a micro-electro-mechanical system inertial measurement unit (MEMS-IMU), Global Positioning System (GPS) and magnetometer integrated navigation system, the procedure of initial alignment is a necessity. However, both magnetometer and GPS receiver are susceptible to external complex environment. Outliers in the observations of the two devices are inevitable. In order to reduce the influence of the outliers and improve the initial alignment accuracy, a sequential robust Kalman filter (SRKF) algorithm based on the square dimension of the innovation vector is proposed. The proposed SRKF algorithm can reasonably decrease the contribution of outlier affected elements of the observation vector in an element-wise way. Robust factor, used to suppress the influence of outliers, is constructed according to the Mahalanobis distance and the Chi-square distribution significance level. The results of different simulations and field tests all indicate the feasibility and the effectiveness of the proposed method.

Highlights

Due to the fact that MEMS-IMU has the advantages of small size, low cost, low power consumption, high reliability and convenient batch production, it has been widely applied to engineering fields and has become an important developing direction for inertial navigation system (Groves 2013; Noureldin et al 2013)
In order to verify the efficiency of the proposed sequential robust Kalman filter (SRKF) algorithm, outliers are added to the simulation data
The outliers to the Global Positioning System (GPS) positions were set as 10 m and 20 m, which obtained at the epoch of 200 s and 400s, respectively

Summary

Introduction

Due to the fact that MEMS-IMU has the advantages of small size, low cost, low power consumption, high reliability and convenient batch production, it has been widely applied to engineering fields and has become an important developing direction for inertial navigation system (Groves 2013; Noureldin et al 2013). In recent years, integrated alignment method has been applied gradually, which includes GPS, magnetometer, odometer, IMU and other sensors (Georgy et al, 2011; Ali and Elsheimy, 2013; Chang et al 2017). With the development of multi-sensor information fusion techniques, MIMU/ GPS/magnetometer integrated navigation system has been widely used in navigation and positioning, which could improve the initial alignment accuracy effectively (Khoder and Jida, 2014). The Kalman filter is a standard algorithm of integrated navigation system. If the process noise and measurement noise are independent Gaussian white noise, the Kalman filter is the optimal algorithm. If sensors were in a complex observation environment, it may cause observation outliers and the observed noise will be displayed as non-Gaussian distribution, the effect of the Kalman filter will decline rapidly

Methods

Results

Conclusion