Kalman Filters and Observers: Modern Applications
Alma Y. Alanís, Nancy Arana‐Daniel, Carlos López-Franco · Wiley Encyclopedia of Electrical and Electronics Engineering · 2018
The Kalman filter (KF) was proposed by Rudolph E. Kalman in the seminal paper “A new approach to linear filtering and prediction problems,” published in 1960. Since then, the KF has found application in many areas, including navigation and control of engineering systems (e.g., airplanes, satellites, and cars), GPS, computer vision, health monitoring, econometrics, and weather forecasting. Theoretical topics include linear and nonlinear filter theory, particle filtering, particle flow methods, geometric approaches and nonlinear observers, filter stability, filtering in high‐dimensional spaces, duality between optimal filtering and control, and estimation over networks. In addition to these classical applications, KF is also used in emerging applications in biology, networks, and artificial intelligence. Although all these applications and theoretical topics are of equal importance, this article focuses on the use of KF in the areas of artificial neural networks (ANNs) and simultaneous localization and mapping