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Proceedings Paper

Optimal matching algorithm for multidimensional sensor readings
Author(s): Richard Ree Brooks; S. Sitharama Iyengar
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Paper Abstract

Multisensor fusion is a method which is of current importance for improving sensor reliability. Because individual sensors are prone to transient errors, mechanical failures, and noise, as well as being of limited accuracy, it is advisable to fuse readings from many heterogeneous sensors. This allows several different sensor technologies to be used together to measure the value of a physical variable. Using a multitude of sensor technologies makes the overall system less sensitive to the failures of any one technology. Unfortunately, it is a non-trivial task to glean the best interpretation from a large number of partially contradictory sensor readings. A number of methods exist for finding the best approximate match for this type of redundant, but possibly faulty, data. This paper presents a new algorithm which finds the best possible interpretation of partially contradictory sensor readings, some of which are incorrect, that contain data of greater than two dimensions. Currently available algorithms return interpretations which are larger than the optimal. This has been done to avoid excessive computational complexity. The algorithm presented here is based on data structures from computational geometry and provides the smallest possible region satisfying the constraints of the problem with a reasonable computational complexity.

Paper Details

Date Published: 15 September 1995
PDF: 9 pages
Proc. SPIE 2589, Sensor Fusion and Networked Robotics VIII, (15 September 1995); doi: 10.1117/12.220948
Show Author Affiliations
Richard Ree Brooks, Louisiana State Univ. (United States)
S. Sitharama Iyengar, Louisiana State Univ. (United States)

Published in SPIE Proceedings Vol. 2589:
Sensor Fusion and Networked Robotics VIII
Paul S. Schenker; Gerard T. McKee, Editor(s)

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