Employing CHNN to Develop a Data Refining Algorithm for Wireless Sensor Networks

In this report a data refining algorithm (DFA) for obtaining the relationships between wireless sensor measurements and existing tracks is proposed. It is known that a DFA plays an important role in wireless sensors for target tracking over WSN (wireless sensor network) deployments. However, a new approach to data refining is here investigated, wherein the matching between mobile sensor measurements and existing target tracks can achieve global consideration. Embedded within the traditional HNN (Hopfield neural networks) is adopted. In this research, the network is guaranteed to converge into a stable state when performing a data association. The HNN-based DFA is combined with mobile sensors in a WSN system to demonstrate the target tracking capabilities. Finally, computer simulation results indicate that this approach successfully solves the data association problems addressed over WSN environments.