Robust Attitude Estimation from Uncertain Observations of Inertial Sensors Using Covariance Inflated Multiplicative Extended Kalman Filter

Ghobadi, Mostafa; Singla, Puneet; Esfahani, Ehsan T.

doi:10.1109/tim.2017.2761230

Cited by 68 publications

(32 citation statements)

References 24 publications

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“…The Kalman linear estimation technique is recognized as an optimal minimum mean square error (MMSE) state estimation method [39,40]. Such method estimates the unknown states based on recursive calculations lead to update the state estimate and its covariance as follows.…”

Section: A Kalman State Estimation Cyclementioning

confidence: 99%

“…These off-the-shelf measurements suffer from the large amount of biases, drifts and immense noise sequences. These problems have been recently tackled by developing sensor fusion techniques, such as the Kalman-based state estimation techniques [12,13]. Integrating such techniques with highly nonlinear systems, such as RUAVs, to improve their attitude estimation and control has gained a great attention in the last few years [12].…”

mentioning

confidence: 99%

“…These problems have been recently tackled by developing sensor fusion techniques, such as the Kalman-based state estimation techniques [12,13]. Integrating such techniques with highly nonlinear systems, such as RUAVs, to improve their attitude estimation and control has gained a great attention in the last few years [12]. For instance, the conventional linear Kalman filter is used to resolve the problem of the attitude estimation of the Maxi Joker flaybarless helicopter [14].…”

mentioning

confidence: 99%

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Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation

Al-Sharman

Zweiri

Jaradat

et al. 2020

IEEE Trans. Instrum. Meas.

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Achieving precise state estimation is needed for the unmanned aerial vehicle to perform a successful flight with a high degree of stability. Nonetheless, obtaining accurate state estimation is considered challenging due to the inaccuracies associated with the measurements of the onboard commercial-offthe-shelf (COTS) Inertial Measurement Unit (IMU). The immense vibration of the vehicle's rotors makes these measurements suffer from issues like; large drifts, biases and immense unpredictable noise sequences. These issues cannot be significantly tackled using classical estimators and an accurate sensor fusion technique needs to be developed. In this paper, a deep learning framework is developed to enhance the performance of the state estimator. A deep neural network (DNN) is trained using a deep-learning-based technique to identify the associated measurement noise models and filter them out. Dropout technique is adopted for training the DNN to avoid overfitting and reduce the complexity of nets computations. Compared to the classical estimation results, the proposed deep learning technique demonstrates capabilities in identifying the measurement's noise characteristics. As an example, an enhancement in estimating the attitude states at near hover is proved using this approach. Furthermore, an actual hover flight was performed to validate the proposed estimation enhancement method.

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Section: A Kalman State Estimation Cyclementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation

Al-Sharman

Zweiri

Jaradat

et al. 2020

IEEE Trans. Instrum. Meas.

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“…In Ref. [25], a inflated covariance method based on multiplicative EKF (MEKF) is proposed to compensate the undesired effects of magnetic distortion and body acceleration. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…[26], the Hidden Markov Model (HMM) is combined with MEKF to estimate the observation covariance matrix, thus compensating the undesired effects of magnetic distortion and linear acceleration. In spite of demonstrated good performance with numerically simulated cases [25] and actual data [26], these algorithms suffer the same limitation of MEKF, which is restricted to small estimate errors.…”

Section: Introductionmentioning

confidence: 99%

Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter

Chiella

Teixeira

Pereira

2019

Sensors

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This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature.

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Precision blade deflection measurement system using wireless inertial sensor nodes

Grundkötter

Melbert

2021

Wind Energy

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Structural health monitoring on wind turbine blades is a great enhancement for operational safety and failure avoidance. Blade deflection is a key parameter, which is a function of blade geometry and material properties. The absolute blade deflection is affected by wind load, gravitational, and centrifugal forces and must exceed neither material stress limits nor tower clearance. In this paper, we introduce a novel method for real time reconstruction of blade movement using inertial measurement units (IMUs) located at defined positions along the blade. An algorithm is introduced that combines IMU measurements and a priori information of the fundamental blade mode shapes to accurately reconstruct the blade movement. A low‐power wireless sensor node for blade deflection monitoring is presented. The autonomous sensor node is equipped with an IMU together with a low‐power wireless transceiver and powered by a compact translational electromagnetic energy harvester. Sufficient energy is harvested each rotor revolution for continuous measurement data streaming within the wind turbine's operating range. A data stream of six‐axis IMU measurements enables real time, three‐dimensional reconstruction of the entire blade movement in the back end positioned in the tower. The reconstruction of blade tip deflection is possible even under high wind turbulences with a minimum accuracy of 0.22 m for an onshore 5 MW and 0.58 m for an offshore 15‐MW reference turbine. The complete system has been tested thoroughly under conditions similiar to a real wind turbine. Realistic stimulation of the reconstruction algorithm is performed by means of OpenFAST simulations.

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Robust Attitude Estimation from Uncertain Observations of Inertial Sensors Using Covariance Inflated Multiplicative Extended Kalman Filter

Cited by 68 publications

References 24 publications

Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation

Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation

Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter

Precision blade deflection measurement system using wireless inertial sensor nodes

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