An engine for fusing data from multiple sensors for classification is provided in this paper. Two novel methods for fusing multiple representations of data with boosting are presented and empirically evaluated against other fusion techniques as candidate algorithms for the fusion engine. We argue that information fusion from sensors operating in complementary regions of the spectrum and/or spatially separated can improve the classification performance.
|Number of pages||5|
|Journal||IEEE National Radar Conference - Proceedings|
|Publication status||Published - 22 Jun 2015|
|Event||2015 IEEE International Radar Conference, RadarCon 2015 - Arlington, United States|
Duration: 10 May 2015 → 15 May 2015
- data fusion