Adaptive Kalman filter for MEMS-IMU based attitude estimation under external acceleration and parsimonious use of gyroscopes

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

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Adaptive Kalman filter for MEMS IMU data fusion using enhanced covariance scaling

Control Theory and Technology ◽

10.1007/s11768-021-00058-8 ◽

2021 ◽

Author(s):

Fuseini Mumuni ◽

Alhassan Mumuni

Keyword(s):

Kalman Filter ◽

Data Fusion ◽

Adaptive Kalman Filter ◽

Mems Imu

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Robust Adaptive Filter for Small Satellite Attitude Estimation Based on Magnetometer and Gyro

Abstract and Applied Analysis ◽

10.1155/2014/159149 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Zhankui Zeng ◽

Shijie Zhang ◽

Yanjun Xing ◽

Xibin Cao

Keyword(s):

Kalman Filter ◽

Attitude Estimation ◽

Optimal Filtering ◽

System State ◽

Small Satellite ◽

Adaptive Kalman Filter ◽

Satellite Attitude ◽

Observational Noise ◽

Robust Adaptive ◽

The Impact

Based on magnetometer and gyro measurement, a sequential scheme is proposed to determine the orbit and attitude of small satellite simultaneously. In order to reduce the impact of orbital errors on attitude estimation, a robust adaptive Kalman filter is developed. It uses a scale factor and an adaptive factor, which are constructed by Huber function and innovation sequence, respectively, to adjust the covariance matrix of system state and observational noise, change the weights of predicted and measured parameters, get suitable Kalman filter gain and approximate optimal filtering results. Numerical simulations are carried out and the proposed filter is approved to be robust for the noise disturbance and parameter uncertainty and can provide higher accuracy attitude estimation.

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Linear Kalman Filter for Attitude Estimation from Angular Rate and a Single Vector Measurement

Journal of Sensors ◽

10.1155/2017/9560108 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Shangqiu Shan ◽

Zhongxi Hou ◽

Jin Wu

Keyword(s):

Kalman Filter ◽

Attitude Determination ◽

Angular Rate ◽

Attitude Estimation ◽

Estimation Accuracy ◽

Kinematic Equation ◽

Vector Measurement ◽

Vector Observation ◽

Execution Speed ◽

External Acceleration

In this paper, a new Kalman filtering scheme is designed in order to give the optimal attitude estimation with gyroscopic data and a single vector observation. The quaternion kinematic equation is adopted as the state model while the quaternion of the attitude determination from a strapdown sensor is treated as the measurement. Derivations of the attitude solution from a single vector observation along with its variance analysis are presented. The proposed filter is named as the Single Vector Observation Linear Kalman filter (SVO-LKF). Flexible design of the filter facilitates fast execution speed with respect to other filters with linearization. Simulations and experiments are conducted in the presence of large external acceleration and magnetic distortion. The results show that, compared with representative filtering methods and attitude observers, the SVO-LKF owns the best estimation accuracy and it consumes much less time in the fusion process.

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