A Real-Time Adaptive High-Gain EKF, Applied to a Quadcopter Inertial Navigation System

Sebesta, Kenneth; Boizot, Nicolas

doi:10.1109/tie.2013.2253063

Cited by 120 publications

(57 citation statements)

References 23 publications

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“…In this work we implemented the so-called extended Kalman filter (EKF for short) (see e.g. [6,42,38]) which is well-known for its robustness w.r.t. the noise.…”

Section: Description Of the Algorithmmentioning

confidence: 99%

Lyapunov and Minimum-Time Path Planning for Drones

et al. 2014

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In this paper, we study the problem of controlling an unmanned aerial vehicle (UAV) to provide a target supervision and/or to provide convoy protection to ground vehicles.We first present a control strategy based upon a Lyapunov-LaSalle stabilization method to provide supervision of a stationary target. The UAV is expected to join a pre-designed admissible circular trajectory around the target which is itself a fixed point in the space.Our strategy is presented for both HALE (High Altitude Long Endurance) and MALE (Medium Altitude Long Endurance) types UAVs. A UAV flying at a constant altitude (HALE type) is modeled as a Dubins vehicle (i.e. a planar vehicle with constrained turning radius and constant forward velocity). For a UAV that might change its altitude (MALE type), we use the general kinematic model of a rigid body evolving in R 3 . Both control strategies presented are smooth and unlike what is usually proposed in the literature these strategies asymptotically track a circular trajectory of exact minimum turning radius.We also present the time-optimal control synthesis for tracking a circle by a Dubins vehicle. This optimal strategy, although much simpler than the point-to-point time-optimal strategy obtained by P. Souères and J.-P. Laumond in the 1990s (see [45]), is very rich.Finally, we propose control strategies to provide supervision of a moving target, that are based upon the previous ones.

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“…In this work we implemented the so-called extended Kalman filter (EKF for short) (see e.g. [6,42,38]) which is well-known for its robustness w.r.t. the noise.…”

Section: Description Of the Algorithmmentioning

confidence: 99%

Lyapunov and Minimum-Time Path Planning for Drones

et al. 2014

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“…Nowadays, the different attitude estimation algorithms have been developed, which are employed to Micro IMUs and integrated with magnetic sensors, Global navigation satellite system (GNSS) receiver, and other sensors [1][2][3][4][10][11][12][13][14][15][16][17]. Among them, representative researches like multiinformation fusion technique utilize the different MEMS sensor [10][11][12], different Kalman filtering methods for attitude estimation [3,[13][14][15], and methodology combining data fusion with filtering strategies [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Among them, representative researches like multiinformation fusion technique utilize the different MEMS sensor [10][11][12], different Kalman filtering methods for attitude estimation [3,[13][14][15], and methodology combining data fusion with filtering strategies [16,17]. Utilizing barometer and low cost IMU, the data fusion method via the complementary filter is proposed to fuse altitude data for unmanned aerial vehicle (UAV) system [10].…”

Section: Introductionmentioning

confidence: 99%

“…Road tests are implemented [13]. According to practical application of quadcopter UAV, the adaptive gain parameter in EKF filter was tuned [14]. The attitude estimation algorithm is developed by incorporating a nonlinear observer in order to fuse measurements from both the gyroscope and accelerometer.…”

Section: Introductionmentioning

confidence: 99%

“…It is restricted in such micro navigation system. For traditional extended Kalman filter and other improved Kalman filter methods for attitude estimation, it would cause the inaccurate estimation or even divergence because the statistical properties of process and measurement noise cannot be predicted accurately, especially for low cost MEMS-based sensors [3,13,14]. Some complicated nonlinear filter and optimal control methods could obtain optimal estimate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

Wang

Song

Han

et al. 2017

Mathematical Problems in Engineering

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For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.

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Distributed adaptive high‐gain extended Kalman filtering for nonlinear systems

Rashedi

Liu

Huang

2017

Intl J Robust & Nonlinear

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Summary In this work, we propose a distributed adaptive high‐gain extended Kalman filtering approach for nonlinear systems. Specifically, we consider a class of nonlinear systems that are composed of several subsystems interacting with each other via their states. In the proposed approach, an adaptive high‐gain extended Kalman filter is designed for each subsystem. The distributed Kalman filters communicate with each other to exchange estimated subsystem state information. First, assuming continuous communication among the distributed filters within deterministic form of subsystems, an implementation strategy that specifies how the distributed filters should communicate is designed and the detailed design of the subsystem filter is described. Second, we consider the case of stochastic subsystems for which the designed subsystem filters communicate to exchange information at discrete‐time instants. A state predictor in each subsystem filter is used to provide predictions of states of other subsystems. The stability properties of the proposed distributed estimation schemes with both continuous and discrete communications are analyzed. Finally, the effectiveness and applicability of the proposed schemes are illustrated via the application to a chemical process example. Copyright © 2017 John Wiley & Sons, Ltd.

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A Real-Time Adaptive High-Gain EKF, Applied to a Quadcopter Inertial Navigation System

Cited by 120 publications

References 23 publications

Lyapunov and Minimum-Time Path Planning for Drones

Lyapunov and Minimum-Time Path Planning for Drones

Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

Distributed adaptive high‐gain extended Kalman filtering for nonlinear systems

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