A decentralized approach to sensory data integration

In this paper, a decentralized approach based on the team consensus approach and Markovian model is proposed to integrate multisensory data. A team of sensors can estimate the local and global uncertainties utilizing self-entropy and conditional-entropy measures of the sensors. Consensus can be reached based on the initial expected values and “uncertainty” weights assigned by the sensors. The proposed approach is compared with the Bayesian approach via experiments on two independent sensors. Results showed that consensus reached are comparable. However, there are factors that indicated the decentralized approach requires less communication and computational effort to reach consensus among sensors