review articles

KALMAN FILTERING IS a state estimation technique used in many application areas such as spacecraft navigation, motion planning in robotics, signal processing, and wireless sensor networks because of its ability to extract useful information from noisy data and its small computational and memory requirements. 12, 20, 27–29 Recent work has used Kalman filtering in controllers for computer systems. 5, 13, 14, 23

Although many introductions to Kalman filtering are
available in the literature, 1–4, 6–11, 17, 21, 25, 29 they are usually
focused on particular applications such as robot motion
or state estimation in linear systems, making it difficult to
see how to apply Kalman filtering to other problems. Other
presentations derive Kalman filtering as an application
of Bayesian inference, assuming that
noise is Gaussian. This leads to the
common misconception that Kalman
filtering can be applied only if noise
is Gaussian. 15

Abstractly, Kalman filtering can be seen as a particular approach to combining approximations of an unknown value to produce a better approximation. Suppose we use two devices of different

An
Elementary
Introduction
to Kalman
Filtering
DOI: 10.1145/3363294

Demystifying the uses of a powerful tool for uncertain information.

BY YAN PEI, SWARNENDU BISWAS, DONALD S. FUSSELL, AND KESHAV PINGALI

key insights

˽ This article presents an elementary derivation of Kalman filtering, a classic state estimation technique.

˽ Understanding Kalman filtering is useful for more principled control of computer systems.

˽ Kalman filtering is used as a black box by many computer scientists.