Distance-Based Decision Fusion in a Distributed Wireless Sensor Network

Target classification fusion problem in a distributed, wireless sensor network is investigated. We propose a distance-based decision fusion scheme exploiting the relationship between sensor to target distance, signal to noise ratio and classification rate, which requires less communication while achieving higher region classification rate when compared to conventional majority-vote-based fusion schemes. Several different methods are tested, and very encouraging simulation results using real world experimental data samples are also observed.