A simple and efficient suboptimal multilevel quantization approach in geographically distributed sensor systems

In this study, the problem of decision fusion in geographically distributed sensor system is considered. The distributed detection system employs several geographically separated local sensors and a fusion center. The local sensors monitor the same object scene and pass their local decisions about the same hypothesis to the fusion center. The fusion center combines all the local decisions into a final global decision. A simple and efficient multilevel quantization and fusion approach, wherein each sensor provides the fusion center with a soft-decision rather than a hard-decision or sensor observation is proposed. Each soft-decision is represented by a multiple-bit decision. The performance of the proposed model is evaluated and compared to the performances of the centralized and the optimum decentralized distributed detection systems. The proposed model is illustrated with several examples highlighting the behavior of the model and benefits of fusing the soft-decisions. We show that the proposed model significantly outperforms the optimum decentralized model, in which each sensor provides only a hard-decision, when two bits per decisions are used. We also show that the performance of the proposed model is reasonably close to the centralized model, in which each sensor provides its observations, when four bits per decisions are used.