An Elementary Introduction to Kalman Filtering

TL;DR

This paper provides an accessible introduction to Kalman filtering, explaining its core principles and demonstrating its application to linear systems, making it easier for newcomers to understand and apply the technique.

Contribution

It offers a clear, conceptual explanation of Kalman filtering separated from specific applications, enhancing understanding for beginners in the field.

Findings

Clarifies the abstract principles of Kalman filtering.

Demonstrates application to linear systems.

Simplifies understanding for newcomers.

Abstract

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.