Bayesian Filtering and Smoothing
Simo Särkkä
- Vol. 3, pp I-XXII, 1
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TLDR
This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework and learns what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages.Abstract:
Now in its second edition, this accessible text presents a unified Bayesian treatment of state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. The book focuses on discrete-time state space models and carefully introduces fundamental aspects related to optimal filtering and smoothing. In particular, it covers a range of efficient non-linear Gaussian filtering and smoothing algorithms, as well as Monte Carlo-based algorithms. This updated edition features new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering by enabling approximations, posterior linearization filtering, and the corresponding smoothers. Coverage of key topics is expanded, including extended Kalman filtering and smoothing, and parameter estimation. The book's practical, algorithmic approach assumes only modest mathematical prerequisites, suitable for graduate and advanced undergraduate students. Many examples are included, with Matlab and Python code available online, enabling readers to implement algorithms in their own projects.read more
Citations
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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Yi Li,Yi Li,Yi Li,Kailong Liu,Aoife Foley,Alana Aragon Zulke,Alana Aragon Zulke,Maitane Berecibar,Elise Nanini-Maury,Joeri Van Mierlo,Harry E. Hoster,Harry E. Hoster +11 more
TL;DR: This review categorises data-driven battery health estimation methods according to their underlying models/algorithms and discusses their advantages and limitations, then focuses on challenges of real-time battery health management and discuss potential next-generation techniques.
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Using Inertial Sensors for Position and Orientation Estimation
TL;DR: In recent years, micro-machined electromechanical system inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost.
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Applied Stochastic Differential Equations
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TL;DR: The topic of this book is stochastic differential equations (SDEs), which are differential equations that produce a different “answer” or solution trajectory each time they are solved, and the emphasis is on applied rather than theoretical aspects of SDEs.
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A Systematization of the Unscented Kalman Filter Theory
TL;DR: With the proposed systematization of the Unscented Kalman Filter theory, the symmetric sets of sigma points in the literature are formally justified, and the proposed SRUKF has improved computational properties when compared to state-of-the-art methods.
References
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Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.