Bayesian approach to multisensor data fusion with Pre- and Post-Filtering

Data provided by sensors is always affected by some level of uncertainty or lack of certainty in the measurements. Combining data from several sources using multisensor data fusion algorithms exploits the data redundancy to reduce this uncertainty. This paper proposes an approach to multisensor data fusion that relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches namely: Pre-Filtering, Post-Filtering and Pre-Post-Filtering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study of estimating the position of a mobile robot using optical encoder and Hall-effect sensor is presented. Experimental study shows that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data in both centralized and decentralized data fusion architectures.