Open Access
贝叶斯滤波与平滑 (Bayesian filtering and smoothing)
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:
Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. 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. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.read more
Citations
More filters
Calculation of Gauss quadrature rules.
Gene H. Golub,John H. Welsch +1 more
TL;DR: Two algorithms for generating the Gaussian quadrature rule defined by the weight function when: a) the three term recurrence relation is known for the orthogonal polynomials generated by $\omega$(t), and b) the moments of the weightfunction are known or can be calculated.
Journal ArticleDOI
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.
Book
State Estimation for Robotics
TL;DR: In this paper, the authors present common sensor models and practical advice on how to carry out state estimation for rotations and other state variables, including batch estimation, the Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection and continuous-time trajectory estimation.
Journal ArticleDOI
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.
Book
Applied Stochastic Differential Equations
Simo Särkkä,Arno Solin +1 more
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.
References
More filters
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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.
Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.