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Advances in Artificial Intelligence
Volume 2013 (2013), Article ID 241260, 11 pages
Research Article

Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion

1Low and Medium Voltage Division, SIEMENS, Cairo, Egypt
2IEEE Senior Member, Engineering Science Department, Suez University, Suez, Egypt

Received 27 May 2013; Revised 2 September 2013; Accepted 4 September 2013

Academic Editor: Djamel Bouchaffra

Copyright © 2013 Waleed A. Abdulhafiz and Alaa Khamis. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the individual sensor. This paper presents an approach to multisensor data fusion in order to decrease data uncertainty with ability to identify and handle inconsistency. The proposed approach relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study to find the position of a mobile robot by estimating its x and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data.