Introduction and Implementations of the Kalman Filter

• Other Authors: Govaers, Felix (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: IntechOpen 2019
• Subjects: Signal processing; extended kalman filter, autonomous vehicles, target tracking, identification, maximum likelihood
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.