Introduction and Implementations of the Kalman Filter
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 localiz...
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| Format: | Electronic Book Chapter |
| Language: | English |
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IntechOpen
2019
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| Subjects: | |
| Online Access: | DOAB: download the publication DOAB: description of the publication |
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| Summary: | 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. |
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| Physical Description: | 1 electronic resource (128 p.) |
| ISBN: | intechopen.75731 9781838805371 9781838805364 9781838807399 |
| Access: | Open Access |