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Possibilities of Using Kalman Filters in Indoor Localization
Mathematics, 2020 Kalman filters are a set of algorithms based on the idea of a filter described by Rudolf Emil Kalman in 1960. Kalman filters are used in various application domains, including localization, object tracking, and navigation.
Katerina Fronckova, Pavel Prazak
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Sequential Covariance Intersection Fusion Robust Time-Varying Kalman Filters with Uncertainties of Noise Variances for Advanced Manufacturing [PDF]
Micromachines, 2022 This paper addresses the robust Kalman filtering problem for multisensor time-varying systems with uncertainties of noise variances. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper ...
Wenjuan Qi, Shigang Wang
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A Review of Dynamic Phasor Estimation by Non-Linear Kalman Filters
IEEE Access, 2022 Phasor estimation under dynamic conditions has been under study recently by relaxing the amplitude and phase of the static phasor. This paper will review some methods to estimate dynamic phasor by nonlinear Kalman filters.
Jalāl Khodaparast
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IEEE Transactions on Automatic Control, 2009 In this paper, we present a new nonlinear filter for high-dimensional state estimation, which we have named the cubature Kalman filter (CKF). The heart of the CKF is a spherical-radial cubature rule, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear Bayesian filter. Specifically, we derive a third-
I Arasaratnam, Simon Haykin
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KalmanFormer: using transformer to model the Kalman Gain in Kalman Filters [PDF]
Frontiers in NeuroroboticsIntroductionTracking the hidden states of dynamic systems is a fundamental task in signal processing. Recursive Kalman Filters (KF) are widely regarded as an efficient solution for linear and Gaussian systems, offering low computational complexity ...
Siyuan Shen +4 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
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Double hybrid Kalman filtering for state estimation of dynamical systems [PDF]
ITM Web of Conferences, 2019 In this paper authors present a new approaches to the hybrid Kalman filtering and modified hybrid Kalman filtering, with the changed order of methods inside (Unscented Kalman Filter and Extended Kalman Filter).
Michalski Jacek +2 more
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The Salted Kalman Filter: Kalman filtering on hybrid dynamical systems
Automatica, 2021 Many state estimation and control algorithms require knowledge of how probability distributions propagate through dynamical systems. However, despite hybrid dynamical systems becoming increasingly important in many fields, there has been little work on utilizing the knowledge of how probability distributions map through hybrid transitions.
Nathan J. Kong +3 more
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Physica D: Nonlinear Phenomena, 2001 The Kalman filter combines forecasts and new observations to obtain an estimation which is optimal in the sense of a minimum average quadratic error. The Kalman filter has two main restrictions: (i) the dynamical system is assumed linear and (ii) forecasting errors and observational noises are taken Gaussian.
Sornette, Didier, Ide, Kayo
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Controlling balance in an ensemble Kalman filter [PDF]
Nonlinear Processes in Geophysics, 2014 We present a method to control unbalanced fast dynamics in an ensemble Kalman filter by introducing a weak constraint on the imbalance in a spatially sparse observational network.
G. A. Gottwald
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