Automatic Adaptation in Classification Algorithms Fusing Data from Heterogeneous Sensors

Robert Marek Nowak , Jacek Misiurewicz , Rafał Biedrzycki

Abstract

In a heterogeneous multisensor environment, data fusion can help to improve the reliability of the sensor data. With respect to classification of tracked objects, features detected by different types of sensors can supplement each other, creating more reliable classifications results. This paper presents an idea of adapting the classification rules in a learning process without "known good" learning data. Instead, the fused result of classification by different classifiers is used for learning. This way, the classification rules are refined in absence of a prior knowledge. The gain lies in the fact that the fused
Author Robert Marek Nowak ISE
Robert Marek Nowak,,
- The Institute of Electronic Systems
, Jacek Misiurewicz ISE
Jacek Misiurewicz,,
- The Institute of Electronic Systems
, Rafał Biedrzycki ISE
Rafał Biedrzycki,,
- The Institute of Electronic Systems
Pages1993-1999
Book Vladimir Vapnik (eds.): Proceedings of the 14th International Conference on Information Fusion, 2011
Keywords in Englishadaptive multisensor fusion engine, Bayes methods, classification algorithm, DAFNE fusion engine, DAFNE sensor simulator, data fusion, distributed multisensor fusion engine, heterogeneous multisensor environment, naive Bayes classifier, sensor fusion
projectResearch on measurment, circuit and signal theory and electronic circuits and systems. Project leader: Romaniuk Ryszard, , Phone: +48 22 234 7986, +48 22 234 5360, start date 05-04-2012, planned end date 31-12-2012, end date 30-11-2013, ISE/2012/DS, Completed
WEiTI Działalność statutowa
Languageen angielski
Score (nominal)10
Citation count*5 (2018-06-16)
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