Robust information filter for decentralized estimation

This paper presents a robust information filter which inherits the structural simplicity of the information filter and the robustness property of the H∞H∞ filter with respect to noise statistics. In this filter, an assurance level γγ on the noise bound is reflected in the newly defined covariance matrix. It provides robustness against uncertainty in the noise model by sacrificing performance in the minimum variance sense. All these are achieved while retaining the simple structure of the standard information filter. Thus, it is able to realize robust decentralized estimation with less communication and computational load while without the need to model the system noise accurately.