Adaptive Robust Nonlinear Filtering for Spacecraft Attitude Estimation Based on Additive Quaternion

Qiu, Zhenbing; Huang, Yulong; Qian, Huaming

doi:10.1109/tim.2019.2894046

Cited by 27 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, the carrier is required to be relatively gentle in movement most of the time. Refer to [28] for a detailed description of the system definition. In this section, a two-step cascaded IKF containing the GD algorithm is introduced.…”

Section: Methodsmentioning

confidence: 99%

A Robust Indirect Kalman Filter Based on the Gradient Descent Algorithm for Attitude Estimation During Dynamic Conditions

Sun

Ding

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The real-time response and accuracy of the attitude (i.e., roll and pitch) estimation from lowcost inertial measurement unit (IMU) have become the key issues restricting related applications. This paper proposes a robust attitude estimation scheme which can perform well under dynamic conditions. When only accelerometers are used to calculate and correct the attitude, the external acceleration becomes the main source of attitude estimation errors. Moreover, the truncation error in the linearization process of the nonlinear system also affects the attitude estimation. As our first contribution, the external acceleration is modeled as a first-order Gauss Markov model, and its value is calculated under the indirect Kalman filter (IKF) framework. The measurement noise covariance matrix of the IKF is adaptively adjusted to enhance its robustness and reduce the negative impact caused by inaccurate modeling. In the second part of our work, the two-step cascade filter method is used for attitude estimation. The attitude obtained from the gravity field based on the gradient descent (GD) algorithm shows fast response capabilities, and hence, it is embedded as a measurement in the IKF by using the chain-derivation rule. The truncation error introduced into the linearization process of the nonlinear system is effectively avoided. Both simulation and experiments are carried out to verify the feasibility and accuracy of the proposed algorithm. The results show that the approach proposed in this paper can meet the accuracy requirements of consumer products.

show abstract

Section: Methodsmentioning

confidence: 99%

A Robust Indirect Kalman Filter Based on the Gradient Descent Algorithm for Attitude Estimation During Dynamic Conditions

Sun

Ding

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It implies that the filter can exhibit poor convergence or even diverge in some situations [15]. To deal with these shortcomings, state-of-the-art KFs utilize assorted artifice, such as unscented KF [16], invariant KF [17], adaptive tuning of the covariance matrices [12], [18], [19]. It is noteworthy that all these optimal stochastic estimators cannot ensure the global convergence in a deterministic context.…”

Section: B Related Workmentioning

confidence: 99%

Uniting Attitude Estimation With Global Asymptotic Stability

Tong

Yan

Cheng

2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

This paper approaches the problem of attitude estimation using biased gyro and body-referenced measurements of inertial vectors. First, a new observer for attitude estimation is proposed through introducing a family of synergistic potential functions with explicit expression of synergistic gap. Furthermore, it is convenient to adjust the design by adding additional state-dependent gains without adapting the switching rules. The proposed observer exhibits faster convergence for large attitude estimation errors than similar work in literature. Second, to achieve better steady-state performance, we unite this global observer with a traditional local observer, i.e., multiplicative extended Kalman filter. The new uniting observer inherits the desired transient and steady-state performance from the two subobservers respectively. The performance of proposed methods has been evaluated via simulation and experiments.

show abstract

“…Therefore, it is essential to develop algorithms capable of accurately predicting head orientation in VR and AR systems. In addition to AR and VR, orientation prediction plays a vital role in various practical applications, such as unmanned aerial vehicles [10], robotics [11], [12], and navigation systems [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Orientation Prediction for VR and AR Devices Using Inertial Sensors Based on Kalman-Like Error Compensation

Dao

Nhat

Hong³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

We propose an orientation prediction algorithm based on Kalman-like error compensation for virtual reality (VR) and augmented reality (AR) devices using measurements of an inertial measurement unit (IMU), which includes a tri-axial gyroscope and a tri-axial accelerometer. First, the initial prediction of the orientation is estimated by assuming linear movement. Then, to improve the prediction accuracy, the accuracies of previous predictions are taken into account by computing the orientation difference between the current orientation and previous prediction. Finally, we define a weight matrix to determine the optimal adjustments for predictions corresponding to a given orientation, which is obtained by minimizing the estimation errors based on the minimum mean square error (MMSE) criterion using Kalman-like error compensation. Experimental results demonstrate that the proposed algorithm exhibits higher orientation prediction accuracy compared with conventional algorithms on several open datasets.INDEX TERMS Orientation prediction, inertial measurement units (IMUs), motion-to-photon (MTP) latency, virtual reality (VR), augmented reality (AR), attitude and heading reference system (AHRS), minimum mean square error (MMSE).

show abstract

Adaptive Robust Nonlinear Filtering for Spacecraft Attitude Estimation Based on Additive Quaternion

Cited by 27 publications

References 31 publications

A Robust Indirect Kalman Filter Based on the Gradient Descent Algorithm for Attitude Estimation During Dynamic Conditions

A Robust Indirect Kalman Filter Based on the Gradient Descent Algorithm for Attitude Estimation During Dynamic Conditions

Uniting Attitude Estimation With Global Asymptotic Stability

Orientation Prediction for VR and AR Devices Using Inertial Sensors Based on Kalman-Like Error Compensation

Contact Info

Product

Resources

About