Flight path reconstruction – A comparison of nonlinear Kalman filter and smoother algorithms

Teixeira, Bruno Otávio Soares; Tôrres, Leonardo A. B.; Iscold, Paulo; Aguirre, Luís A.

doi:10.1016/j.ast.2010.07.005

Cited by 33 publications

(13 citation statements)

References 28 publications

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“…We employed an unscented Kalman filter (UKF) to determine biases associated to each sensor in the inertial measurement unit and to reconstruct UAV trajectory from low frequency GPS data. The results were compared with an extended Kalman filter, which was outperformed in all aspects [14].…”

Section: Attitude and Heading Reference Systemmentioning

confidence: 99%

On the development of a small hand-held multi-UAV platform for surveillance and monitoring

Avellar

Thums

Lima

et al. 2013

2013 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper addresses the ongoing process of development of a small hand-held multiple unmanned aerial vehicle platform for surveillance and monitoring. The project cover all phases of development, from aerodynamic design and mechanical assembly, through fine tuning low level control loops and design of an attitude and heading reference system, up to implementation of navigation algorithms and finally flight testing. Commercial hardware is used to speed up development, allowing the team responsible for implementing the navigation algorithms to execute and test while the team responsible for the aerodynamics and electronics can design and compare results. I. INTRODUCTIONSmall Unnamed Aerial Vehicles (SUAVs) are a class of robotic airships, which equipped with cameras and special sensors, can be used in several applications. The importance of these vehicles has grown continuously in the last years, especially for military and reconnaissance purposes, particularly in operations where the tasks are dangerous or tedious for human pilots. A special type of SUAV is constituted by hand launched fixed-wing air vehicles that can be easily operated by a single user in an outdoor environment. For this vehicles, several applications exist, such as forest fire monitoring [1], multispectral agricultural remote sensing and crop management [2], urban environment monitoring [3], real time mapping of disaster areas [4], power line inspection [5] and many others.

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Section: Attitude and Heading Reference Systemmentioning

confidence: 99%

On the development of a small hand-held multi-UAV platform for surveillance and monitoring

Avellar

Thums

Lima

et al. 2013

2013 International Conference on Unmanned Aircraft Systems (ICUAS)

Self Cite

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“…In a recent article [16] a comparison of both the EKF and UKF is done for the particular case of flight path reconstruction for a fixed-wing UAV. This article includes an interesting discussion of previous references that illustrate the improved performance of the UKF over EKF estimating the attitude of an UAV.…”

Section: Background and Related Workmentioning

confidence: 99%

UAV Attitude Estimation Using Unscented Kalman Filter and TRIAD

Marina

Pereda

Girón-Sierra

et al. 2012

IEEE Trans. Ind. Electron.

192

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A main problem in autonomous vehicles in general, and in Unmanned Aerial Vehicles (UAVs) in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Three Axis Attitude Determination (TRIAD) algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the Extended Kalman Filter (EKF). The paper presents field experiment results using a real fixed-wing UAV. The results show good real-time performance with low computational cost in a microcontroller.

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“…Namely, the analytical approximation (or function approximation) methods, the positive sampling methods, and the random sampling methods [7][8][9][10]. The representatives for each class are the extended Kalman filter (EKF) [11][12][13], the sigma point Kalman filter (SPKF) [14][15][16], and the particle filter [17], respectively. EKF is known to be effective to the systems that are linear or near linear, which has poor robustness.…”

Section: Introductionmentioning

confidence: 99%

State estimation for asynchronous multirate multisensor nonlinear dynamic systems with missing measurements

Yan

Xiao

Xia

et al. 2012

Adaptive Control & Signal

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This paper is concerned with the state estimation for a kind of nonlinear multirate multisensor asynchronous sampling dynamic system. There are N sensors observing a single target independently at multiple sampling rates, and the dynamic system is formulated at the highest sampling rate. Observations are obtained asynchronously, and each sensor may lose data randomly at a certain probability. The fused state estimate is generated using multiscale system theory and the modified sigma point Kalman filter. It is shown that our main results improve and extend the existing sigma point Kalman filter for which the samples are obtained multirate nonuniformly. Measurements randomly missing with Bernoulli distribution could also be allowed in this paper. Finally, the feasibility and efficiency of the presented algorithm is illustrated by a numerical simulation example.

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Flight path reconstruction – A comparison of nonlinear Kalman filter and smoother algorithms

Cited by 33 publications

References 28 publications

On the development of a small hand-held multi-UAV platform for surveillance and monitoring

On the development of a small hand-held multi-UAV platform for surveillance and monitoring

UAV Attitude Estimation Using Unscented Kalman Filter and TRIAD

State estimation for asynchronous multirate multisensor nonlinear dynamic systems with missing measurements

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