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240202 ||| eng |
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|a intechopen.75731
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|a 9781838805371
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|a 9781838805364
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|a 9781838807399
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|a Govaers, Felix
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|a Introduction and Implementations of the Kalman Filter
|h Elektronische Ressource
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| 260 |
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|b IntechOpen
|c 2019
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| 300 |
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|a 1 electronic resource (128 p.)
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| 653 |
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|a extended kalman filter, autonomous vehicles, target tracking, identification, maximum likelihood
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| 653 |
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|a Digital signal processing (DSP) / bicssc
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| 700 |
1 |
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|a Govaers, Felix
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/3.0/
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| 024 |
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|a 10.5772/intechopen.75731
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|u https://directory.doabooks.org/handle/20.500.12854/130683
|z DOAB: description of the publication
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| 856 |
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|u https://mts.intechopen.com/storage/books/7466/authors_book/authors_book.pdf
|7 0
|x Verlag
|3 Volltext
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| 520 |
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|a 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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