TY  - CHAP
AU  - Rajamani Doraiswami
AU  - Lahouari Cheded
ED  - Felix Govaers
Y1  - 2019-04-30
PY  - 2019
T1  - Novel Direct and Accurate Identification of Kalman Filter for General Systems Described by a Box-Jenkins Model
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. 6
UR  - https://doi.org/10.5772/intechopen.81793
DO  - 10.5772/intechopen.81793
SN  - 978-1-83880-537-1
PB  - IntechOpen
CY  - Rijeka
Y2  - 2024-04-20
ER  -