TY - CHAP A1 - Youngjoo Kim A2 - Hyochoong Bang ED1 - Felix Govaers Y1 - 2019-05-22 PY - 2019 T1 - Introduction to Kalman Filter and Its Applications N2 - 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. BT - Introduction and Implementations of the Kalman Filter SP - Ch. 2 UR - https://doi.org/10.5772/intechopen.80600 DO - 10.5772/intechopen.80600 SN - 978-1-83880-537-1 PB - IntechOpen CY - Rijeka Y2 - 2020-11-24 ER -