Attitude estimation and sensor identification utilizing nonlinear filters based on a low-cost MEMS magnetometer and sun sensor

Teshnizi, Masoud Mirzaei; Shirazi, Abolfazl

doi:10.1109/maes.2015.150069

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…The first belongs to the realm of unscented filtering, which shows a performance improvement in terms of convergence properties. Unscented filtering has also been studied for CubeSat attitude estimation [14]. The second is part of a category termed two-step filtering.…”

Section: Introductionmentioning

confidence: 99%

A Hardware Implementation of Flexible Attitude Determination and Control System for Two-Axis-Stabilized CubeSat

Gaber

Mashade

Aziz

2020

J. Electr. Eng. Technol.

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This paper presents a hardware implementation of flexible and low-cost attitude determination and control system (ADCS) for two-axis-stabilized CubeSat. As small satellite missions are increasing, the CubeSat requires precise ADCS with attitude drift adjustment. This attitude drift if not properly compensated, will cause a slow attitude information loss as the error in attitude rises between the actual and estimated signals. The proposed ADCS comprises two steps; the attitude determination which estimates the current CubeSat's attitude and a novel simplified intelligent proportional-integral control algorithm that accurately adjusts the attitude. The control algorithm is based on the multi degree-of-freedom controller concept and has no controller gains parameters. The proposed ADCS employs sun sensor, magnetometer, and a micro-electro-mechanical gyroscope sensor to correct the attitude drift by offering a comparative attitude that is utilized for updating the estimated attitude delivered to the Kalman filter for determining the CubeSat's attitude and angular velocity. The ADCS model verification and validation are accomplished via Matlab/Simulink and hardware implementation. A comparison with other ADCS techniques is presented. The ADCS simulated model demonstrates precision results with error of less than 0.1°.

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Section: Introductionmentioning

confidence: 99%

A Hardware Implementation of Flexible Attitude Determination and Control System for Two-Axis-Stabilized CubeSat

Gaber

Mashade

Aziz

2020

J. Electr. Eng. Technol.

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“…Mao et al [10] proposed a method for accurate attitude determination based on data fusion of a three-axis gyroscope, a three-antenna GPS, and a star sensor. Tetanize and Shirazi [11] combined low-cost MEMS and a nonlinear attitude estimation algorithm to design an inexpensive and accurate system support for navigation and attitude determination.…”

Section: Introductionmentioning

confidence: 99%

Combinatory Attitude Determination Method for High Rotational Speed Rigid-Body Aircraft

Wang

Liu

et al. 2020

Mathematical Problems in Engineering

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In this paper, we present a novel multisensor combinatory attitude determination method that enables high-accuracy measurement of the attitude of a high rotational speed rigid-body aircraft. We analyze the external moments of the aircraft during flight and develop the method using theoretical deductions based on the motion equations of a rigid body rotating around the centroid. The proposed method fuses the data measured from GPS, gyrometer, and magnetometer and uses the improved unscented Kalman filter (UKF) algorithm to perform filtering. First, appropriate assumptions and simplifying approximations are made for around-centroid motion equations of a rigid body according to the motion characteristics of the high rotational speed aircraft. Using these assumptions and approximations, the constraint equations between the Euler attitude angles and flight-path angle, trajectory deflection angle are derived to serve as the state equation. Second, the roll angle with error is calculated using the geomagnetic field model and the geomagnetic intensity measured by a three-axis magnetometer and then fused with the angular velocity information obtained from the gyroscope for constructing the measurement equations. Finally, the state equations are discretized using the Runge–Kutta method during the UKF prediction stage, improving the prediction accuracy. Simulation results show that the proposed method can effectively determine the attitude information of the high rotational speed aircraft, achieving high level of reliability and accuracy thanks to the combination of information from GPS, gyroscope, and magnetometer.

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“…e traditional EKF and other improved EKF have the drawbacks such as low estimation accuracy and poor stability, which will cause the biased estimation or even divergence due to which the statistical properties of system and measurement noise cannot be predicted accurately, especially for low-cost MEMS-based sensors [21]. Some complicated nonlinear calculation will cause a heavy burden in real-time micronavigation system, which will weaken the feasibility for application [2,22]. erefore, the advanced filtering or adaptive filtering methods will play a key role in these applications [6,13,17,23].…”

Section: Introductionmentioning

confidence: 99%

Accurate Attitude Determination Based on Adaptive UKF and RBF Neural Network Using Fusion Methodology for Micro-IMU Applied to Rotating Environment

Wang

Meng

Guan

2020

Mathematical Problems in Engineering

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Focusing on the issue of attitude tracking for low-cost and small-size Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU) in high dynamic environment, an Adaptive Unscented Kalman Filter (AUKF) method combining sensor fusion methodology with Artificial Neural Network (ANN) is proposed. The different control strategies are adopted by fusing multi-MEMS inertial sensors under various dynamic situations. The AUKF attitude determination approach utilizing the MEMS sensor and Global Positioning System (GPS) can provide reliable estimation in these situations. In particular, the adaptive scale factor is used to adaptively weaken or enhance the effects on new measurement data according to the predicted residual vector in the estimation process. In order to solve the problem that the new measurement data is not available in case of GPS fault, an attitude algorithm based on Radial Basis Function (RBF)-ANN feedback correction is proposed for AUKF. The estimated deviation of predicted system state can be provided based on RBF-ANN in GPS-denied environment. The corrected predicted system state is used for the estimation process in AUKF. An experimental platform was setup to simulate the rotation of the spinning projectile. The experimental results show that the proposed method has better performance in terms of attitude estimation than other representative methods under various dynamic situations.

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Attitude estimation and sensor identification utilizing nonlinear filters based on a low-cost MEMS magnetometer and sun sensor

Cited by 11 publications

References 12 publications

A Hardware Implementation of Flexible Attitude Determination and Control System for Two-Axis-Stabilized CubeSat

A Hardware Implementation of Flexible Attitude Determination and Control System for Two-Axis-Stabilized CubeSat

Combinatory Attitude Determination Method for High Rotational Speed Rigid-Body Aircraft

Accurate Attitude Determination Based on Adaptive UKF and RBF Neural Network Using Fusion Methodology for Micro-IMU Applied to Rotating Environment

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