"Introduction and Implementations of the Kalman Filter" ed. by Felix Govaers

This volume is dedicated to the process of combining error-prone, heterogeneous, incomplete, and ambiguous data to gather a higher level of situational awareness.

Sensor data fusion is the process of combining error-prone, heterogeneous, incomplete, and ambiguous data to gather a higher level of situational awareness. In principle, all living creatures are fusing information from their complementary senses to coordinate their actions and to detect and localize danger. In sensor data fusion, this process is transferred to electronic systems, which rely on some "awareness" of what is happening in certain areas of interest. By means of probability theory and statistics, it is possible to model the relationship between the state space and the sensor data. The number of ingredients of the resulting Kalman filter is limited, but its applications are not.

Contents
1.Introductory Chapter: Kalman Filter - The Working Horse of Object Tracking Systems Nowadays
2.Introduction to Kalman Filter and Its Applications
3.Tuning of the Kalman Filter Using Constant Gains
4.Statically Fused Converted Measurement Kalman Filters
5.A Scalable, FPGA-Based Implementation of the Unscented Kalman Filter
6.Novel Direct and Accurate Identification of Kalman Filter for General Systems Described by a Box-Jenkins Model