A least squares fusion rule in multiple sensors distributed detection systems

In this paper, a new least square data fusion rule in multiple sensor distributed detection system is proposed. In the proposed approach, the central processor combines the sensors hard decisions through least squares criterion to make the global hard decision of the central processor. In contrast to the optimum Neyman-Pearson fusion, where the distributed detection system is optimized at the fusion center level or at the sensors level, but not simultaneously, the proposed approach achieves global optimization at both the fusion center and at the distributed sensors levels. This is done without knowing the error probabilities of each individual distributed sensor. Thus the proposed least squares fusion rule does not rely on any stability of the noise environment and of the sensors false alarm and detection probabilities. Therefore, the proposed least squares fusion rule is robust and achieves better global performance. Furthermore, the proposed method can easily be applied to any number of sensors and any type of distributed observations. The performance of the proposed least squares fusion rule is evaluated and compared to the optimum Neyman-Pearson fusion rule. The results show that the performance of the proposed least squares fusion rule outperforms the performance of the Neyman-Pearson fusion rule.