Title :
A fuzzy approach to low level sensor fusion with limited system knowledge
Author :
Sebastian Blank;Tobias Föhst;Karsten Berns
Author_Institution :
Robotics Research Lab, Dep. of Computer Sciences, University of Kaiserslautern, Kaiserslautern, Germany
fDate :
7/1/2010 12:00:00 AM
Abstract :
When facing the need to perform low-level sensor fusion with only very limited knowledge available one has to come up with an alternative to the well known and proven Kalman filter. A very interesting candidate for such applications is the so called fuzzy (or soft) voter. This algorithm makes use of fuzzy logic principles to fuse signals in an efficient way and provides a figure of merit as well as sensor monitoring capabilities with very moderate demand for computation performance and memory. In this paper a computational efficient alternative implementation of soft voting for embedded applications is described. Furthermore, its performance is examined using scenarios typical to harsh operations environments using simulation.
Keywords :
"Sensitivity","Accuracy","Fuzzy sets","Kalman filters","Sensor fusion","Robot sensing systems","Computational modeling"
Conference_Titel :
Information Fusion (FUSION), 2010 13th Conference on
DOI :
10.1109/ICIF.2010.5711876