Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach

Saved in:

Bibliographic Details
Published in:	arXiv.org (Feb 28, 2017), p. n/a
Main Author:	Kasthurirengan Suresh
Other Authors:	Silva, Samuel, Votion, Johnathan, Cao, Yongcan
Published:	Cornell University Library, arXiv.org
Subjects:	Algorithms Localization Artificial intelligence Sensors Traffic surveillance Cluster analysis Clustering Traffic management Target recognition Multiple target tracking Machine learning Pattern recognition Computer simulation Vector quantization Search and rescue missions
Online Access:	Citation/Abstract Full text outside of ProQuest
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Abstract:	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.
ISSN:	2331-8422
Source:	Engineering Database