Overview

Kalman Filtering is an algorithm that provides estimates of unknown variables over time, using a series of measurements observed over time, which may include noise or other inaccuracies. It provides optimal estimates by minimizing the mean of the squared error. The filter operates recursively, processing each new measurement to update estimates of the system's current state and predicting future states. It is widely used in control systems, navigation, and signal processing due to its efficiency in handling uncertain data and its ability to incorporate new information dynamically. Key to its operation are two stages, namely, prediction, which forecasts the system's next state, and update, which adjusts the forecast based on the latest measurement. This iterative process refines the state estimates continuously, making the Kalman Filter an essential tool for real-time applications such as GPS, robotics, and financial modeling. This book is a compilation of chapters that discuss the most vital concepts and emerging trends in the field of Kalman Filtering. The aim of this book is to present researches that have transformed this discipline and aided its advancement. This book is meant for students who are looking for an elaborate reference text on the subject.

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