Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms\r\nreduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data,\r\ncatastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of\r\neach of the individual sensor. This paper presents an approach tomultisensor data fusion in order to decrease data uncertainty with\r\nability to identify and handle inconsistency.Theproposed approach relies on combining a modified Bayesian fusion algorithm with\r\nKalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how\r\nfiltering 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\r\nx and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling\r\nthe problem of uncertainty and inconsistency of the data.
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