Results 51 to 60 of about 7,693 (163)

Deep Kalman Filters

CoRR, 2015
17 pages, 14 figures: Fixed typo in Fig.
Rahul G. Krishnan, Uri Shalit, David A. Sontag   +2 more
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A Class of Quaternion Kalman Filters [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2014
The existing Kalman filters for quaternion-valued signals do not operate fully in the quaternion domain, and are combined with the real Kalman filter to enable the tracking in 3-D spaces. Using the recently introduced HR-calculus, we develop the fully quaternion-valued Kalman filter (QKF) and quaternion-extended Kalman filter (QEKF), allowing for the ...
Cyrus Jahanchahi, Danilo P. Mandic
openaire   +2 more sources

Robust hybrid Neural–Kalman filter for real-time supercapacitor state-of-charge estimation in electric vehicles

Future Batteries
Accurate estimation of supercapacitor state-of-charge (SOC) is vital for optimal energy management in electric vehicles (EVs), particularly within hybrid energy storage systems (HESS). Challenges arise from nonlinear dynamics, self-discharge, temperature
Islam A. Sayed, Yousef Mahmoud
doaj   +1 more source

Wavelet ensemble Kalman filters [PDF]

Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems, 2011
4 pages, 4 ...
Jonathan D. Beezley, Jan Mandel, Loren Cobb   +2 more
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Mixture Kalman Filters

Journal of the Royal Statistical Society Series B: Statistical Methodology, 2000
Summary In treating dynamic systems, sequential Monte Carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the on-line ‘filtering’ task. We propose a special sequential Monte Carlo method, the mixture Kalman filter, which
Chen, Rong, Liu, Jun S.
openaire   +2 more sources

Adaptive Measurement Noise for Robust Kalman Filtering in Smart Beehive Telemetry

IEEE Access
Honeybee colony monitoring generates multimodal, non-stationary telemetry streams that require reliable recursive state estimation with well-calibrated uncertainty for digital apiculture.
H. A. A. U. Ranasinghe, Navod Neranjan Thilakarathne, Jamil Abedalrahim Jamil Alsayaydeh, Rex Bacarra, Rostam Affendi Hamzah   +4 more
doaj   +1 more source

An Introduction to Kalman Filters

Measurement + Control, 1986
Kalman filters are a powerful tool for reducing the effects of noise in measurements. This paper gives a no-nonsense introduction to the subject for people with A-level maths.
G C Dean
doaj   +1 more source

Geometry of Kalman Filters [PDF]

, 2012
In this paper is presented a geometric explanation of Kalman filters in terms of a symplectic linear space and a special quadratic form on it. It is an extension of the work of Bougerol with application of a different metric introduced earlier. The author's purpose in this paper is to show that both contraction properties can be understood purely in ...
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Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances

Journal of Applied Mathematics, 2014
A direct approach of designing weighted fusion robust steady-state Kalman filters with uncertain noise variances is presented. Based on the steady-state Kalman filtering theory, using the minimax robust estimation principle and the unbiased linear ...
Wen-Juan Qi, Peng Zhang, Zi-Li Deng
doaj   +1 more source

Robust Kalman Filtering [PDF]

, 2000
As already pointed out in Hardle, Klinke, and Muller (2000, Chapter 10), state-space models are very useful and flexible in the sense that various recursive methods for time-dependent situations can be formulated as general solutions of filtering, smoothing and prediction problems in state-space models.
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