Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach
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| Опубликовано в:: | arXiv.org (Feb 28, 2017), p. n/a |
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| Главный автор: | |
| Другие авторы: | , , |
| Опубликовано: |
Cornell University Library, arXiv.org
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| Предметы: | |
| Online-ссылка: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 2074475871 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2074475871 | ||
| 045 | 0 | |b d20170228 | |
| 100 | 1 | |a Kasthurirengan Suresh | |
| 245 | 1 | |a Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach | |
| 260 | |b Cornell University Library, arXiv.org |c Feb 28, 2017 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Data-target pairing is an important step towards multi-target localization for the intelligent operation of unmanned systems. Target localization plays a crucial role in numerous applications, such as search, and rescue missions, traffic management and surveillance. The objective of this paper is to present an innovative target location learning approach, where numerous machine learning approaches, including K-means clustering and supported vector machines (SVM), are used to learn the data pattern across a list of spatially distributed sensors. To enable the accurate data association from different sensors for accurate target localization, appropriate data pre-processing is essential, which is then followed by the application of different machine learning algorithms to appropriately group data from different sensors for the accurate localization of multiple targets. Through simulation examples, the performance of these machine learning algorithms is quantified and compared. | |
| 653 | |a Algorithms | ||
| 653 | |a Localization | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Sensors | ||
| 653 | |a Traffic surveillance | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Clustering | ||
| 653 | |a Traffic management | ||
| 653 | |a Target recognition | ||
| 653 | |a Multiple target tracking | ||
| 653 | |a Machine learning | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Computer simulation | ||
| 653 | |a Vector quantization | ||
| 653 | |a Search and rescue missions | ||
| 700 | 1 | |a Silva, Samuel | |
| 700 | 1 | |a Votion, Johnathan | |
| 700 | 1 | |a Cao, Yongcan | |
| 773 | 0 | |t arXiv.org |g (Feb 28, 2017), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2074475871/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/1703.00084 |