Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors

Marina, Héctor García de; Espinosa, Felipe; Santos, Carlos

doi:10.3390/s120709566

Cited by 46 publications

(29 citation statements)

References 24 publications

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“…Most fit into one of two categories: frequency-domain filters such as complimentary filters [6][7][8] or statistical filters such as the extended Kalman filter [9], unscented Kalman filter [10], etc. Gyroscopes provide good high-frequency attitude information but poor lowfrequency information due to integration of noise and bias drift.…”

Section: B Attitude and Heading Reference Systems Algorithmsmentioning

confidence: 99%

Small Unmanned Aerial System Attitude Estimation for Flight in Wind

Weibel

Lawrence

Palo

2015

Journal of Guidance, Control, and Dynamics

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Section: B Attitude and Heading Reference Systems Algorithmsmentioning

confidence: 99%

Small Unmanned Aerial System Attitude Estimation for Flight in Wind

Weibel

Lawrence

Palo

2015

Journal of Guidance, Control, and Dynamics

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“…e basis of vibration environment simulation is to acquire vibration information during vehicle driving. Vibration information includes vehicle attitude and vibration displacement [22][23][24]. e calculating algorithm for an attitude angle is the core to obtain vehicle attitude, an important factor that affects the record accuracy of vibration information.…”

Section: Introductionmentioning

confidence: 99%

Vibration Information Acquisition and Representation Control Strategy for Construction Vehicle Driver Seat

Gong

Zhao²,

Wang

2019

Shock and Vibration

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Vibration information acquisition and representation control strategy in the experiment of construction of vehicle driver seat are investigated in this study. An improved adaptive algorithm of multisensor attitude data fusion is proposed. The nonlinear equations of vehicle attitude quaternions are constructed by using the test information of accelerometers and magnetometers. Then, a complementary filter is used to fuse the obtained attitude with gyroscope information to obtain attitude information during vehicle travel. Weight matrix and filter coefficients in the algorithm are adjusted with vehicle motions to ensure accuracy of attitude measurement under static, low-, and high-acceleration conditions. The vehicle vibration displacement information is reconstructed by the low-frequency attenuation integration method on the basis of attitude measurement to inhibit integral trend items and reduce vibration displacement reconstruction errors. The three-state controller is designed to control a six-degree-of-freedom hydraulic simulated vibration test bed, and the obtained vibration signal is used as an excitation signal to perform a vibration representation control test. Finally, results verify that the proposed method demonstrates favourable data fusion and representation accuracy.

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“…Thus, a series of algorithms, which can be divided into three kinds, are studied for vehicle attitude estimation. One of the kinds are those algorithms based on vector observations [5,6,7,8,9], such as the TRIAD algorithm [6], QUEST algorithm [7], FOAM algorithm [8] and SVD algorithm [9]. These algorithms are deduced according to some specific physical or geometric significance.…”

Section: Introductionmentioning

confidence: 99%

Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction

Xing

Hang

Xiong

et al. 2016

Sensors

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This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions.

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Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors

Cited by 46 publications

References 24 publications

Small Unmanned Aerial System Attitude Estimation for Flight in Wind

Small Unmanned Aerial System Attitude Estimation for Flight in Wind

Vibration Information Acquisition and Representation Control Strategy for Construction Vehicle Driver Seat

Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction

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