TY  - CHAP
AU  - Mudambi R. Ananthasayanam
ED  - Felix Govaers
Y1  - 2018-11-23
PY  - 2018
T1  - Tuning of the Kalman Filter Using Constant Gains
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. 3
UR  - https://doi.org/10.5772/intechopen.81795
DO  - 10.5772/intechopen.81795
SN  - 978-1-83880-537-1
PB  - IntechOpen
CY  - Rijeka
Y2  - 2024-04-20
ER  -