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...
Сохранить в:
| Формат: | Online |
|---|---|
| Язык: | английский |
| Опубликовано: |
IntechOpen
2023
|
| Предметы: | |
| Online-ссылка: | ONIX_20231201_9781838805371_1792 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
|
| _version_ | 1869526396670312448 |
|---|---|
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-130683 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1306832024-04-14T10:28:30Z Introduction and Implementations of the Kalman Filter Govaers, Felix extended kalman filter, autonomous vehicles, target tracking, identification, maximum likelihood thema EDItEUR::U Computing and Information Technology::UY Computer science::UYS Digital signal processing (DSP) 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. 2023-12-01T18:06:28Z 2023-12-01T18:06:28Z 2019 book ONIX_20231201_9781838805371_1792 9781838805371 9781838805364 9781838807399 https://directory.doabooks.org/handle/20.500.12854/130683 eng image/jpeg n/a https://www.intechopen.com/books/7466 https://mts.intechopen.com/storage/books/7466/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/intechopen.75731 10.5772/intechopen.75731 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9781838805371 9781838805364 9781838807399 IntechOpen 128 open access |
| spellingShingle | extended kalman filter, autonomous vehicles, target tracking, identification, maximum likelihood thema EDItEUR::U Computing and Information Technology::UY Computer science::UYS Digital signal processing (DSP) Introduction and Implementations of the Kalman Filter |
| title | Introduction and Implementations of the Kalman Filter |
| title_full | Introduction and Implementations of the Kalman Filter |
| title_fullStr | Introduction and Implementations of the Kalman Filter |
| title_full_unstemmed | Introduction and Implementations of the Kalman Filter |
| title_short | Introduction and Implementations of the Kalman Filter |
| title_sort | introduction and implementations of the kalman filter |
| topic | extended kalman filter, autonomous vehicles, target tracking, identification, maximum likelihood thema EDItEUR::U Computing and Information Technology::UY Computer science::UYS Digital signal processing (DSP) |
| topic_facet | extended kalman filter, autonomous vehicles, target tracking, identification, maximum likelihood thema EDItEUR::U Computing and Information Technology::UY Computer science::UYS Digital signal processing (DSP) |
| url | ONIX_20231201_9781838805371_1792 |