Sensor data fusion with support vector machine techniques
Jerome J. Braun · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
This paper presents an approach to multisensor data fusion based on the use of Support Vector Machines (SVM). The approach is investigated using simulated generic sensor data, representative of data imperfections that may be encountered in multisensor fusion applications. In particular the issue of data incompleteness is addressed and a method exploiting vicinity of training points is proposed for incompleteness correction. The paper also investigates applicability of vicinal kernels in SVM-based sensor data fusion.