Introduction and implementations of the kalman filter / / edited by Felix Govaers.
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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| Place / Publishing House: | London, England : : IntechOpen,, [2019] ©2019 |
| Year of Publication: | 2019 |
| Language: | English |
| Physical Description: | 1 online resource (128 pages) :; illustrations |
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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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| ISBN: | 183880739X 1838805370 |
| Hierarchical level: | Monograph |
| Statement of Responsibility: | edited by Felix Govaers. |