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A Data Fusion Methodology for Wireless Sensor Systems

An efficient DFA (data fusion algorithm) plays an important role in tracking for moving objects over WSS (wireless sensor system) deployments in order to track the objects accurately. Accuracy in object tracking is mainly dominated by the prediction for those moving targets by filtering and refining the results from wireless mobile sensors deployed in WSS environment. A DFA based on CHHN (competitive Hopfield neural network) technique for obtaining the relationship between measurements results from wireless mobile sensors and estimation of existing tracks over WSS (wireless sensor system) is proposed in this paper. Embedded within the CHNN is also a competitive learning mechanism which creatively removes the dilemma of occasional irrational solutions in traditional HNN (Hopfield neural networks). In this research, except the proposed approach is established with CHNN, the methodology of data fusion over WSS is guaranteed to converge into a stable state when performing a data association. In words, the CHNN-based DFA is combined with wireless mobile sensors in a WSS environment to demonstrate the target tracking capabilities. Computer simulation results illustrate that the new methodology of data fusion based on CHNN is not only successfully able to solve the data association problems addressed over WSS environments, but the specified simulated targets can also be tracked without large scale missing.