In this report a data fusion 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 fusion based on the CHNN (competitive Hopfield neural network) is here investigated, wherein the matching between mobile sensor measurements and existing target tracks can achieve global consideration. 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, it is also established that with the proposed approach, the network is guaranteed to converge into a stable state when performing a data association. The CHNN-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.