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Enhanced Indoor Mobile Robot Localization via Lie-Group IMU-UWB Fusion and Dual-Stage Kalman Filtering. [PDF]
Sensors (Basel)He Z, Tang X, Li M, Zhang F.
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Particle Filtering-Based In-Flight Icing Detection for Unmanned Aerial Vehicles. [PDF]
Sensors (Basel)Souanef T +4 more
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Low-Cost Portable Sensor Node for Gas and Chemical Leak Detection with Kalman-Filtering-Based UWB Localization. [PDF]
Sensors (Basel)Al-Okby MFR, Roddelkopf T, Thurow K.
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High-Precision Laser Time-Frequency Synchronization in Space Based on an Improved Kalman Filtering Method. [PDF]
Sensors (Basel)Sun B, Wang X, Sun Z, Zheng F.
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Nonlinear filtering with exogenous Kalman filter and double Kalman filter
2016 European Control Conference (ECC), 2016We propose two variants of the Linearized Kalman Filter (LKF). The model linearization is made about an auxiliary state estimate that can be seen as an exogenous input to the LFK, and the resulting two-stage estimator is called an exogenous Kalman filter (XKF).
Tor Arne Johansen, Thor I. Fossen
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Neurocomputing, 2005
Anticipating future events is a crucial function of the central nervous system and can be modelled by Kalman filter-like mechanisms, which are optimal for predicting linear dynamical systems. Connectionist representation of such mechanisms with Hebbian learning rules has not yet been derived.
Gábor Szirtes +2 more
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Anticipating future events is a crucial function of the central nervous system and can be modelled by Kalman filter-like mechanisms, which are optimal for predicting linear dynamical systems. Connectionist representation of such mechanisms with Hebbian learning rules has not yet been derived.
Gábor Szirtes +2 more
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A Quadratic Kalman Filter [PDF]
SSRN Electronic Journal, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Monfort, A. +2 more
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Asian Journal of Control, 2016
AbstractIn the Extended Kalman Filter (EKF), only the first‐order term of the Taylor series is employed. Hence, the nonlinearities in the system dynamics are not fully considered. In the proposed method, to overcome this drawback, the higher‐order terms of the Taylor series are considered and a new filter, based on the Modal series, is designed.
Mohammaddadi, Gh. +2 more
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AbstractIn the Extended Kalman Filter (EKF), only the first‐order term of the Taylor series is employed. Hence, the nonlinearities in the system dynamics are not fully considered. In the proposed method, to overcome this drawback, the higher‐order terms of the Taylor series are considered and a new filter, based on the Modal series, is designed.
Mohammaddadi, Gh. +2 more
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Annual Review of Control, Robotics, and Autonomous Systems, 2018
The Kalman filter—or, more precisely, the extended Kalman filter (EKF)—is a fundamental engineering tool that is pervasively used in control and robotics and for various estimation tasks in autonomous systems. The recently developed field of invariant extended Kalman filtering uses the geometric structure of the state space and the dynamics to improve
Bonnabel, Silvere, Barrau, Axel
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The Kalman filter—or, more precisely, the extended Kalman filter (EKF)—is a fundamental engineering tool that is pervasively used in control and robotics and for various estimation tasks in autonomous systems. The recently developed field of invariant extended Kalman filtering uses the geometric structure of the state space and the dynamics to improve
Bonnabel, Silvere, Barrau, Axel
openaire +2 more sources