Ata uhi

A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization

I tiakina i:
I whakaputaina i:arXiv.org (May 30, 2017), p. n/a
Kaituhi matua: Silva, Samuel
Ētahi atu kaituhi: Rengan Suresh, Feng, Tao, Votion, Johnathan, Cao, Yongcan
I whakaputaina:
Cornell University Library, arXiv.org
Ngā marau:
Localization
Neural networks
Traffic surveillance
Multilayers
Error correction
Clustering
Traffic management
Target recognition
Multiple target tracking
Machine learning
Pattern recognition
Computer simulation
Search and rescue missions
Urunga tuihono:Citation/Abstract
Full text outside of ProQuest
Ngā Tūtohu: Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
Whakarāpopotonga: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.
ISSN:2331-8422
Puna:Engineering Database