A Fusion Algorithm for Target Detection in Distributed Sensor Networks

In this article, we propose a target detection method in wireless sensor networks based on distributed data fusion. Firstly, we introduce a tree topology. It is different from the conventional tree topology, the sensors in our topology are assigned with weights which are proportional to the received Signal to Noise Ratio (SNR), and are arranged orderly. So the Fusion Center (FC) achieves the highest SNR. Secondly, we propose a fusion algorithm which takes the channel noise into consideration on the base of this topology. The sensor makes a decision relying to its two children nodes and its own observation. At last, we prove that the probability of detection is optimized (maximized) for a given pre specified small probability of false alarm. Our simulation results show the effectiveness of our algorithm by comparing with other detection approaches and analyzing the performance of our algorithm. Signal to Noise Ratio (SNR), and are arranged orderly. So the Fusion Center (FC) achieves the highest SNR. Secondly, we propose a fusion algorithm which takes the channel noise into consideration on the base of this topology. The sensor makes a decision relying to its two children nodes and its own observation. At last, we prove that the probability of detection is optimized (maximized) for a given pre specified small probability of false alarm. Our simulation results show the effectiveness of our algorithm by comparing with other detection approaches and analyzing the performance of our algorithm.