The purpose of the Kalman filter is to use measurements that are observed over time that contain noise (random variations) and other inaccuracies, and produce values that tend to be closer to the true values of the measurements and their associated calculated values. The Kalman filter has many applications in technology, and is an essential part of the development of space and military technology. This book presents topical research data in the study of Kalman filtering, including Kalman filtering in the detection and analysis of voltage dips, short interruptions and overvoltages in voltage supply; statistical state-space modeling using Kalman filtration; and attitude estimators based on Kalman filtering for application on low Earth orbit microsatellites.
Preface; Optimization of Kalman Filtering Performance in Received Signal Strength Based Mobile Positioning; Application of Kalman Filtering in Power Systems: Harmonic Distortion & Voltage Events; Statistical State-Space Modeling via Kalman Filtration; Forecasting the Weekly US Time-Varying Beta: Comparison between Garch Models & Kalman Filter Method; Ensemble Forecasting through Evolutionary Computing & Data Assimilation: Application to Environmental Sciences; Attitude Determination using Kalman Filtering for Low Earth Orbit Microsatellites; Design of Extended Recursive Wiener Fixed-Point Smoother & Filter in Continuous-Time Stochastic Systems; Kalman Filtering Approach to Blind Separation of Independent Source Components; Using a Restricted Kalman Filtering Approach for the Estimation of a Dynamic Exchange-Rate Pass-Through; Quantized Kalman Filtering of Linear Stochastic Systems; Kalman Filter to Estimate Dynamic & Important Patterns of Interaction between Multiple Variables; Index.