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

Shranjeno v:
izdano v:arXiv.org (Feb 28, 2017), p. n/a
Glavni avtor: Kasthurirengan Suresh
Drugi avtorji: Silva, Samuel, Votion, Johnathan, Cao, Yongcan
Izdano:
Cornell University Library, arXiv.org
Teme:
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
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Resumen: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
Fuente:Engineering Database