An Elementary Introduction to Kalman Filtering
| Autor: | Yan Pei, Keshav Pingali, Donald S. Fussell, Swarnendu Biswas |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
010302 applied physics
Signal processing General Computer Science Computer science Linear system 02 engineering and technology Kalman filter Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control 01 natural sciences Computer Science::Robotics symbols.namesake Basic knowledge Probability theory Computer engineering Gaussian noise 020204 information systems 0103 physical sciences 0202 electrical engineering electronic engineering information engineering symbols FOS: Electrical engineering electronic engineering information engineering Robot State (computer science) |
| DOI: | 10.48550/arxiv.1710.04055 |
| Popis: | Kalman filtering is a classic state estimation technique used in application areas such as signal processing and autonomous control of vehicles. It is now being used to solve problems in computer systems such as controlling the voltage and frequency of processors. Although there are many presentations of Kalman filtering in the literature, they usually deal with particular systems like autonomous robots or linear systems with Gaussian noise, which makes it difficult to understand the general principles behind Kalman filtering. In this paper, we first present the abstract ideas behind Kalman filtering at a level accessible to anyone with a basic knowledge of probability theory and calculus, and then show how these concepts can be applied to the particular problem of state estimation in linear systems. This separation of concepts from applications should make it easier to understand Kalman filtering and to apply it to other problems in computer systems. Comment: Small tweaks |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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