Multisensor data fusion schemes for wireless sensor networks

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. © 2006 IEEE.

Air traffic control; Automation; Computer architecture; Computer science; Computer systems; Computers; Control systems; Control theory; Detectors; Filtration; Fusion reactions; Hybrid sensors; Industrial research; Information fusion; Military data processing; Network architecture; Nuclear physics; Remote sensing; Sensor data fusion; Sensor networks; Sensors; Signal processing; Space optics; Wireless networks; Wireless sensor networks; Air Traffic (CO); conference proceedings; Current state; Data-fusion systems; Filter-based; General (CO); International (CO); Kalman filtering; Machine perception; Mean square (MS) estimator; Multi sensor data fusion; Spatially distributed sensors; Transferable belief model (TBM); Wireless sensors; Data fusion

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