Fault-tolerant Bayesian Decentralized Data Fusion Using Reliability Variables and Mixture Models*

In uncertain and dynamic environments, decentralized data fusion (DDF) techniques have been widely used to estimate the states and the uncertainty levels over large mission spaces in a robust and scalable way. In data fusion frameworks using distributed sensor networks, undetected sensor failures can degrade the quality of fusion results of the entire system. Therefore, DDF methods which are robust to inconsistent data are needed. In this paper, a fault-tolerant Bayesian DDF method using Gaussian mixture models is developed. The probability of agent reliability states, which represent consistency of local estimates that agents share with their neighbors, are modeled as weights of mixture models and estimated together with the target process. The target process and reliability states are updated in a decentralized Bayesian way, exploiting the properties of Gaussian mixture models. To prevent the hypothesis explosion problem of Gaussian mixture models, a mixture compression method considering the physical meaning of mixture weights is utilized. A numerical simulation on a 2D dynamic target tracking problem is presented to verify performance of the suggested algorithm and compared with existing DDF methods. It is shown that the suggested algorithm gives more compact fusion results compared to existing fault-tolerant DDF method.