System Modeling and Performance Analysis for Remote Target Detection of Small-scale Sensor Networks

In view of practical application of the target detection based on sensor networks, the remote target detection model is established. This paper addresses a novel Fusion Rule for Small-scale Sensor networks (FRSS) under the Neyman-Pearson criteria. The detection range or detection performance is improved by the way of data fusion. The fusion statistic (Counting statistic) is derived. Besides, the threshold of fusion center solving model is constructed through the randomized test and the closed-form expression of the detection performance is given. The performance of the target detection system under the ideal channel between local sensor nodes and fusion center and non-ideal channel using BPSK modulation is evaluated by numerical approach. Moreover, the Monte-Carlo approach is used to analyze comparatively the detection performance of FRSS rule and the previous Chair-Varshney rule, Bayes rule. The simulation results show that the proposed FRSS rule exhibits slight decline considering the detection performance compared with the other two rules. However, the FRSS rule requires less prior information and greatly reduces the amount of data transmission. The detection performance of FRSS is extremely improved compared with the single sensor node.