Cascaded Kalman Filtering-Based Attitude and Gyro Bias Estimation With Efficient Compensation of External Accelerations

Muhammad Azeem Javed,Muhammad Tahir,Khurram Ali

doi:10.1109/access.2020.2980016

Abstract

We consider the problem of attitude estimation of rigid bodies in motion using low cost inertial measurement unit (IMU). An efficient scheme is proposed using two different Kalman filters by deriving their measurement models for precise attitude (pitch and roll) estimation in the presence of high and prolonged dynamic conditions and gyro bias. Both filters work in a coupled fashion where one of the filters provides accurate estimates of rigid body attitude and external acceleration using the accelerometer in conjunction with the gyroscope while the second filter is responsible to estimate the gyro bias, allowing the proposed scheme to be used in any application with minimal calibration. A new threshold based external acceleration detection module is also introduced to change the confidence level on external acceleration prediction to assist the estimation process. The proposed scheme is tested and compared with other existing estimators in the literature under different dynamical conditions and real-world experimental data sets in order to validate its effectiveness.

Highlights

The advent of micro electro mechanical systems (MEMS) technology has enabled the development of inertial measurement units (IMU) which due to their low cost, light weight and reduced power consumption, are being widely used in a variety of different applications which require attitude and/or heading information such as human activity and gesture recognition using smart phones and watches [1]–[3], motion stabilization and control using drones and robots [4], [5], space and marine vehicle navigation [6], 3D motion tracking systems [7] and indoor localization [8], [9].An IMU consists of a triad of accelerometer, gyroscope and magnetometer
We propose a cascaded KF based attitude estimator which provides the correct estimates of attitude, external acceleration and gyro bias simultaneously using two different Kalman filters which work in a coupled fashion
External acceleration and gyro bias were added to each axis of sensors to simulate different scenarios

Summary

Introduction

The advent of micro electro mechanical systems (MEMS) technology has enabled the development of inertial measurement units (IMU) which due to their low cost, light weight and reduced power consumption, are being widely used in a variety of different applications which require attitude (pitch and roll) and/or heading (yaw) information such as human activity and gesture recognition using smart phones and watches [1]–[3], motion stabilization and control using drones and robots [4], [5], space and marine vehicle navigation [6], 3D motion tracking systems [7] and indoor localization [8], [9].An IMU consists of a triad of accelerometer, gyroscope and magnetometer. While accelerometer and gyroscope measure the linear and angular motions respectively, the magnetometer measures local earth’s magnetic field vector. A smart phone speaker has a field of about 200μT (4 times more than the earth’s magnetic field). This behavior of magnetometers calls for special calibration techniques to be deployed before using their measurements [10]. Due to this reason, the magnetometers are mostly used for orientation estimation if heading information is needed

Methods

Results

Conclusion