@inproceedings{4f06d2f5bd1646908e102f56a217aa17,
title = "Inferential measurements for situation awareness",
abstract = "The paper proposes a generic approach to building inferential measurement systems. The large amount of data needed to be acquired and processed by such systems necessitates the use of machine learning techniques. In this study, an inferential measurement system aimed at enhancing situation awareness has been developed and tested on simulated traffic surveillance data. The performance of several Computational Intelligence techniques within this system has been examined and compared on the data containing anomalous driving patterns.",
keywords = "anomaly detection, inferential measurement, machine learning, situation awareness, unmanned aerial vehicles",
author = "Prapa Rattadilok and Andrei Petrovski",
year = "2013",
doi = "10.1109/CIVEMSA.2013.6617402",
language = "English",
isbn = "9781467347013",
series = "2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2013 - Proceedings",
pages = "93--98",
booktitle = "2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2013 - Proceedings",
note = "2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2013 ; Conference date: 15-07-2013 Through 17-07-2013",
}