A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization
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| Veröffentlicht in: | arXiv.org (May 30, 2017), p. n/a |
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Cornell University Library, arXiv.org
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| 001 | 2075726381 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2075726381 | ||
| 045 | 0 | |b d20170530 | |
| 100 | 1 | |a Silva, Samuel | |
| 245 | 1 | |a A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization | |
| 260 | |b Cornell University Library, arXiv.org |c May 30, 2017 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Data-target association is an important step in multi-target localization for the intelligent operation of un- manned systems in numerous applications such as search and rescue, traffic management and surveillance. The objective of this paper is to present an innovative data association learning approach named multi-layer K-means (MLKM) based on leveraging the advantages of some existing machine learning approaches, including K-means, K-means++, and deep neural networks. To enable the accurate data association from different sensors for efficient target localization, MLKM relies on the clustering capabilities of K-means++ structured in a multi-layer framework with the error correction feature that is motivated by the backpropogation that is well-known in deep learning research. To show the effectiveness of the MLKM method, numerous simulation examples are conducted to compare its performance with K-means, K-means++, and deep neural networks. | |
| 653 | |a Localization | ||
| 653 | |a Neural networks | ||
| 653 | |a Traffic surveillance | ||
| 653 | |a Multilayers | ||
| 653 | |a Error correction | ||
| 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 Search and rescue missions | ||
| 700 | 1 | |a Rengan Suresh | |
| 700 | 1 | |a Feng, Tao | |
| 700 | 1 | |a Votion, Johnathan | |
| 700 | 1 | |a Cao, Yongcan | |
| 773 | 0 | |t arXiv.org |g (May 30, 2017), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2075726381/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/1705.10757 |