Results 51 to 60 of about 1,778,758 (298)
On the Relation Between Smooth Variable Structure and Adaptive Kalman Filter
Frontiers in Applied Mathematics and Statistics, 2020 This article is addressed to the topic of robust state estimation of uncertain nonlinear systems. In particular, the smooth variable structure filter (SVSF) and its relation to the Kalman filter is studied.
Mark Spiller, Dirk Söffker
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On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters
, 2018 Kalman filter is a key tool for time-series forecasting and analysis. We show that the dependence of a prediction of Kalman filter on the past is decaying exponentially, whenever the process noise is non-degenerate.
Kozdoba, Mark +3 more
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Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters [PDF]
AIP Conference Proceedings, 2010 Optimal nonlinear filtering consists of sequentially determining the conditional probability distribution functions (pdf) of the system state, given the information of the dynamical and measurement processes and the previous measurements. Once the pdfs are obtained, one can determine different estimates, for instance, the minimum variance estimate, or ...
Ibrahim Hoteit +6 more
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Advances in Mechanical Engineering, 2017 The robust parameter estimation of unknown space objects is essential to the on-orbit servicing missions. Based on the adaptive filtering techniques along with the dual quaternions modeling methods for pose estimation, this article proposes a dual vector
Xianghao Hou +3 more
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Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems
Sensors, 2011 Recently, the range of available Radio Frequency Identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings.
Min Chul Kim +5 more
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Robust self-adaptive Kalman filter with application in target tracking
Measurement + Control, 2022 Kalman filter has been applied extensively to the target tracking. The estimation performance of Kalman filter is closely resulted by the quality of prior information about the process noise covariance (Q) and the measurement noise covariance (R ...
Yi-Wei Chen, Ken-Ming Tu
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Consensus+Innovations Distributed Kalman Filter With Optimized Gains [PDF]
IEEE Transactions on Signal Processing, 2016 In this paper, we address the distributed filtering and prediction of time-varying random fields represented by linear time-invariant (LTI) dynamical systems.
Subhro Das, José M. F. Moura
semanticscholar +1 more source
Reports on Geodesy and Geoinformatics, 2015 In navigation practice, there are various navigational architecture and integration strategies of measuring instruments that affect the choice of the Kalman filtering algorithm.
Malinowski Marcin, Kwiecień Janusz
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A Square-Root Kalman Filter Using Only QR Decompositions [PDF]
arXiv, 2022 The Kalman filter operates by storing a Gaussian description of the state estimate in the form of a mean and covariance. Instead of storing and manipulating the covariance matrix directly, a square-root Kalman filter only forms and updates a triangular matrix square root of the covariance matrix.
arxiv
Deep Kalman Filters Can Filter
SSRN Electronic Journal, 2023 Deep Kalman filters (DKFs) are a class of neural network models that generate Gaussian probability measures from sequential data. Though DKFs are inspired by the Kalman filter, they lack concrete theoretical ties to the stochastic filtering problem, thus limiting their applicability to areas where traditional model-based filters have been used, e.g ...
Horvath, Blanka +3 more
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