Communications of the ACM

Review articles

An Elementary Introduction to Kalman Filtering

By Yan Pei, Swarnendu Biswas, Donald S. Fussell, Keshav Pingali
Communications of the ACM, November 2019, Vol. 62 No. 11, Pages 122-133
10.1145/3363294
Comments

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.^{12,20,27,28,29} Recent work has used Kalman filtering in controllers for computer systems.^5,13,14,23

Back to Top

Key Insights

Although many introductions to Kalman filtering are available in the literature,^{1,2,3,4,6,7,8,9,10,11,17,21,25,29} 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

---

No entries found

Log in to Read the Full Article