Attitude heading reference algorithm based on transformed cubature Kalman filter

Yu, Yi; Zhang, Xiang; Khan, M Sadiq Ali

doi:10.1177/0020294020944941

Cited by 11 publications

(9 citation statements)

References 17 publications

(31 reference statements)

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“…As shown in Figure 1(a) and (b), both the horizontal speed and rotation can generate corresponding optical flow. Therefore, in the case that optical flow sensor is attached to UAV and pointed to the ground, as shown in Figure 1(c), the optical flow measurement d can be described by equation (1). In equation ( 1), v is the horizontal velocity of the UAV, l is the height of the optical flow sensor above the ground, g is the angle between the optical axis and the vertical direction, v is the rotation speed of the optical flow sensor, and f is the scale factor…”

Section: Optical Flow Sensormentioning

confidence: 99%

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UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit

Xiao-qin

Shi

et al. 2021

International Journal of Distributed Sensor Networks

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Three-dimensional attitude estimation for unmanned aerial vehicles is usually based on the combination of magnetometer, accelerometer, and gyroscope (MARG). But MARG sensor can be easily affected by various disturbances, for example, vibration, external magnetic interference, and gyro drift. Optical flow sensor has the ability to extract motion information from image sequence, and thus, it is potential to augment three-dimensional attitude estimation for unmanned aerial vehicles. But the major problem is that the optical flow can be caused by both translational and rotational movements, which are difficult to be distinguished from each other. To solve the above problems, this article uses a gated recurrent unit neural network to implement data fusion for MARG and optical flow sensors, so as to enhance the accuracy of three-dimensional attitude estimation for unmanned aerial vehicles. The proposed algorithm can effectively make use of the attitude information contained in the optical flow measurements and can also achieve multi-sensor fusion for attitude estimation without explicit mathematical model. Compared with the commonly used extended Kalman filter algorithm for attitude estimation, the proposed algorithm shows higher accuracy in the flight test of quad-rotor unmanned aerial vehicles.

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Section: Optical Flow Sensormentioning

confidence: 99%

“…Attitude estimation is the premise to ensure stable and controllable flight for unmanned aerial vehicles (UAV). [1][2][3] In recent years, attitude and heading reference systems (AHRS) are widely adopted in UAV flight control. 4 The typical configuration of AHRS includes magnetometer, accelerometer, and gyroscope (MARG).…”

Section: Introductionmentioning

confidence: 99%

UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit

Xiao-qin

Shi

et al. 2021

International Journal of Distributed Sensor Networks

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“…3 Kalman filter has been widely applied to navigation, communication, satellite, target tracking, flight control, and so on. [4][5][6][7][8][9][10] It is essentially one of the most important filter theories and many improved filters are developed based on Kalman filter such as Extended Kalman filter and unscented Kalman filter. 11 The operation of Kalman filter depends on the precise system dynamic model and the statistical property of noise fundamentally.…”

Section: Introductionmentioning

confidence: 99%

Robust self-adaptive Kalman filter with application in target tracking

Chen¹,

Tu²

2022

Measurement and Control

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Kalman filter has been applied extensively to the target tracking. The estimation performance of Kalman filter is closely resulted by the quality of prior information about the process noise covariance (Q) and the measurement noise covariance (R). Therefore, the development of adaptive Kalman filter is mainly to reduce the estimation errors produced by the uncertainty of Q and R. In this paper, the proposed self-adaptive Kalman filter algorithm has solved the problems of covariance-matching method about the determination of the width of the window and the addition of storage burden and that can update Q and R simultaneously. Simulation results confirm that the proposed method outperforms the traditional Kalman filter and has the better estimation performance than the other two adaptive Kalman filters in the target tracking. The developed filtering algorithm has the following characteristics: high robustness, low computing load, easy operation and tuning Q, R simultaneously.

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“…Experimental results show that the proposed scheme is accurate and effective. Similarly, in [10], the study proposes the attitude heading reference algorithm based on the cubature Kalman filter (CKF) for low precision of AHRS. is filter also helps in solving the nonlocal sampling problem.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Extended Kalman Filter for Attitude Estimation of the Fixed-Wing UAV

Xiaoqian

Feicheng

Zheng-bing

et al. 2022

International Journal of Optics

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Flying vehicle’s navigation, direction, and control in real-time results in the design of a strap-down inertial navigation system (INS). The strategy results in low accuracy, performance with correctness. Aiming at the attitude estimation problem, many data fusion or filtering methods had been applied, which fail in many cases, which attains the nonlinear measurement model, process dynamics, and high navigation range. The main problem in unmanned aerial vehicles (UAVs) and flying vehicles is the determination of attitude angles. A novel attitude estimation algorithm is proposed in this study for the unmanned aerial vehicle (UAV). This research article designs two filtering algorithms for fixed-wing UAVs which are nonlinear for the attitude estimation. The filters are based on Kalman filters. The unscented Kalman filter (UKF) and cubature Kalman filter (CKF) were designed with different parameterizations of attitude, i.e., Euler angle (EA) and INS/unit quaternion (UQ) simultaneously. These filters, EA-UKF and INS-CKF, use the nonlinear process and measurement model. The computational results show that among both filters, the CKF attains a high accuracy, robustness, and estimation for the attitude estimation of the fixed-wing UAV.

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Attitude heading reference algorithm based on transformed cubature Kalman filter

Cited by 11 publications

References 17 publications

UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit

UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit

Robust self-adaptive Kalman filter with application in target tracking

Nonlinear Extended Kalman Filter for Attitude Estimation of the Fixed-Wing UAV

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