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

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. INDEX TERMS Attitude estimation, gyro bias, external acceleration, MEMS IMU, Kalman filter.

Center-of-Rotation-Augmented Kalman Filter for Adaptive Attitude Reference System

“…Plentiful works have addressed the means of sensor fusion with respect to ARS and AHRS. The most common approaches, by far, are complementary filter [4], [5], [6], [7], [8], [9], [10] and Kalman filter or its variants [1], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]. Complementary filter is a simple data fusion technique which combines complementary information from two different sensors in the frequency domain.…”

“…Lee [24] proposed a Kalman filter-based ARS that models the external acceleration as a first-order low-pass filtered white noise process. Though such modelling approach is adopted by several works that followed [11], [25], [26], yet, the model is not based on the actual nature of the nongravitational acceleration, lacking justification behind the approach. [27] adopts the model of [24] and employs an augmented Kalman filter to describe the dynamics, similar to our proposed work.…”

A Fast Weakly-Coupled Double-Layer ESKF Attitude Estimation Algorithm and Application

“…) denote the unit quaternion attitude at time t k and t k−1 , respectively. The q q q(∆t k )(∆t k = t k − t k−1 ) can be derived by Equation (7).…”

“…In recent years, the microelectromechanical systems inertial measurement unit (MEMS IMU) [4,5] has been developed rapidly. Due to the advantages [6,7] of being lightweight, small size, low power consumption, and low cost, the MEMS IMU has become an excellent selection for small UAVs in attitude estimation. The UAV attitude estimation system [8,9] is composed of a gyroscope, accelerometer, magnetometer, and microprocessor, and uses information fusion algorithms to estimate the optimal attitude vector, which includes the roll angle, pitch angle, and yaw angle.…”