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

محفوظ في:

التفاصيل البيبلوغرافية
الحاوية / القاعدة:	arXiv.org (May 30, 2017), p. n/a
المؤلف الرئيسي:	Silva, Samuel
مؤلفون آخرون:	Rengan Suresh, Feng, Tao, Votion, Johnathan, Cao, Yongcan
منشور في:	Cornell University Library, arXiv.org
الموضوعات:	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
الوصول للمادة أونلاين:	Citation/Abstract Full text outside of ProQuest
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

الوصف
مستخلص:	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.
تدمد:	2331-8422
المصدر:	Engineering Database