Multisensor Data Fusion Schemes for Wireless Sensor Networks

Abstract

Data fusion systems is an active research field with applications in several fields such as manufacturing, surveillance, air traffic control, robotics and remote sensing. The wide interest in wireless sensor networks has fueled the interest in data fusion as a medium to compress and interpret the collected data from the spatially distributed sensors. The present paper gives a general overview on the current state of data fusion schemes for wireless sensor networks. Specifically this paper presents a review on some of the commonly used techniques such as Kalman filtering, beamforming, transferable belief model, filter-based techniques and linear mean square estimator.