An Estimator for UAV Attitude Determination based on Accelerometers, GNSS Sensors, and Aerodynamic Coefficients

Celis, Raúl de; Cadarso, Luís

doi:10.1002/navi.256

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Thus, the nine-dimensional state variables are chosen as three attitude angle, velocity, and position errors X s = [δφ, δθ, δψ, δV N , δV E , δV U , δL, δλ, δH] T . According to Equation (19), the state model of the navigation reconstruction method can be expressed as…”

Section: State Modelmentioning

confidence: 99%

“…Combining Equations (19) and (32), the discrete model of navigation reconstruction filter aided by vehicle model is obtained as…”

Section: Discrete Forms Of Navigation Reconstruction Filtermentioning

confidence: 99%

“…The possibility of vehicle-model-aided INS is first introduced by [18]. An attitude estimator combining aircraft kinematics and GPS is proposed for fixed-wing UAVs in [19,20]. However, these algorithms mainly concentrate on fixed-wing UAVs and do not take position and velocity estimation into consideration.…”

Section: Introductionmentioning

confidence: 99%

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A Vehicle-Model-Aided Navigation Reconstruction Method for a Multicopter during a GPS Outage

Zhang

et al. 2021

Electronics

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The integrated navigation of inertial navigation systems (INS) and the Global Positioning System (GPS) is essential for small unmanned aerial vehicles (UAVs) such as multicopters, providing steady and accurate position, velocity, and attitude information. Nevertheless, decreasing navigation accuracy is a serious threat to flight safety due to the long-term drift error of INS in the absence of GPS measurements. To bridge the GPS outage for multicopters, this paper proposes a novel navigation reconstruction method for small multicopters, which combines the vehicle dynamic model and micro-electro-mechanical system (MEMS) sensors. Firstly, an induced drag model is introduced into the dynamic model of the vehicle, and an efficient online parameter identification method is designed to estimate the model parameters quickly. Secondly, the body velocity can be calculated from the vehicle model and accelerometer measurement. In addition, the nongravitational acceleration estimated from body velocity and radar height are utilized to yield a more accurate attitude estimate. Fusing the information of the attitude, body velocity, magnetic heading, and radar height, a navigation system based on an error-state Kalman filter is reconstructed. Then, an adaptive measurement covariance algorithm based on a fuzzy logic system is designed to reduce the weight due to the disturbed acceleration. Finally, the hardware-in-loop experiment is carried out to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed navigation reconstruction algorithm aided by the vehicle model can significantly improve navigation accuracy during a GPS outage.

show abstract

Section: State Modelmentioning

confidence: 99%

“…Combining Equations (19) and (32), the discrete model of navigation reconstruction filter aided by vehicle model is obtained as…”

Section: Discrete Forms Of Navigation Reconstruction Filtermentioning

confidence: 99%

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A Vehicle-Model-Aided Navigation Reconstruction Method for a Multicopter during a GPS Outage

Zhang

et al. 2021

Electronics

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“…As in [19], these two articles pay more attention to the improvement of attitude accuracy by quality control. Raúl de Celis (2018) presented a novel approach for gravity vector estimation in a body reference frame, which is based on UAV (unmanned aerial vehicle) aerodynamics and mass properties [20]. Reference [21] introduced the design, implementation and experimental results of a quaternion-based EKF algorithm for attitude and heading determination of multi-rotor UAVs using low-cost MEMS sensors.…”

Section: Introductionmentioning

confidence: 99%

A Novel GNSS Attitude Determination Method Based on Primary Baseline Switching for A Multi-Antenna Platform

et al. 2020

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The global navigation satellite system (GNSS)-based attitude determination system has attracted more and more attention with the advantages of having simplified algorithms, a low price and errors that do not accumulate over time. However, GNSS signals may have poor quality or lose lock in some epochs with the influence of signal fading and the multipath effect. When the direct attitude determination method is applied, the primary baseline may not be available (ambiguity is not fixed), leading to the inability of attitude determination. With the gradual popularization of low-cost receivers, making full use of spatial redundancy information of multiple antennas brings new ideas to the GNSS-based attitude determination method. In this paper, an attitude angle conversion algorithm, selecting an arbitrary baseline as the primary baseline, is derived. A multi-antenna attitude determination method based on primary baseline switching is proposed, which is performed on a self-designed embedded software and hardware platform. The proposed method can increase the valid epoch proportion and attitude information. In the land vehicle test, reference results output from a high-accuracy integrated navigation system were used to evaluate the accuracy and reliability. The results indicate that the proposed method is correct and feasible. The valid epoch proportion is increased by 16.2%, which can effectively improve the availability of attitude determination. The RMS of the heading, pitch and roll angles are 0.52°, 1.25° and 1.16°.

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“…16 It is widely used in the attitude calculation of UAV. 17 The calculation amount is small compared with the direction cosine method, but there is a nonlinear problem. It is required to jointly expanded Kalman filter (EKF) to the data fusion.…”

Section: Introductionmentioning

confidence: 99%

Research on vehicle attitude and heading reference system based on multi-sensor information fusion

Fan

Wang

Lin

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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To improve the accuracy of attitude and heading reference systems for moving vehicles, an effective orientation estimation method is proposed. The method uses an odometer, a low-cost magnetic, angular rate, and gravity sensor. This study addresses the problems of non-orthogonal error, carrier magnetic field interference and calibration to obtain accurate, long-term, stable magnetic field strength. A neural network fusion 12-parameter ellipse fitting method is proposed to eliminate the soft magnetic field and hard magnetic field interference. The interference to the accelerometer from linear acceleration is eliminated by using an odometer and a gyroscope, and the high-frequency noise from the accelerometer is eliminated by using a low-pass filter. An improved method to evaluate vehicle attitude is proposed and utilized to compensate for filtered accelerometer measurement when the vehicle is moving at a uniform, accelerate and steering state. The proposed method uses an effective adaptive Kalman filter based on the error state model to reduce dynamic perturbations. Filter gain is adaptively tuned under different moving modes by adjusting the noise matrix. The effectiveness of the algorithm is verified by experiments and simulations in multiple operating conditions.

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An Estimator for UAV Attitude Determination based on Accelerometers, GNSS Sensors, and Aerodynamic Coefficients

Cited by 5 publications

References 17 publications

A Vehicle-Model-Aided Navigation Reconstruction Method for a Multicopter during a GPS Outage

A Vehicle-Model-Aided Navigation Reconstruction Method for a Multicopter during a GPS Outage

A Novel GNSS Attitude Determination Method Based on Primary Baseline Switching for A Multi-Antenna Platform

Research on vehicle attitude and heading reference system based on multi-sensor information fusion

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