Distributed H$\infty$-Constraint Robust Estimator for Multi-Sensor Networked Hybrid Uncertain Systems
Juan Xia, Shesheng Gao, Guo Li, Xiaomin Qi, Bingbing Gao, Jiahao Zhang
Abstract
Due to the complex communication channel environment, uncertainties, including random measurement delays, missing measurements and uncertain measurement noise variances, often exist in multi-sensor networked hybrid uncertain systems. These uncertainties will lead to poor accuracy and robustness for traditional information fusion methods. Hence, this paper presents a distributed robust estimator based on the H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> constraint to solve the mentioned issues in sensor networked systems. The nonlinear pseudomeasurement model is first constructed with Bernoulli distributed random variables to describe the mentioned uncertainties in the communication channels. Then, a sufficient condition with a local optimal upper bound on estimation error covariance is derived rigorously with respect to the robustness definition, which is based on the H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> constraint inequality to improve the robustness of state estimation. Furthermore, the proposed estimator is proven to be stable through boundedness analysis for the upper bound matrix. The numerical simulation and the target tracking experiment are finally applied to evaluate the effectiveness of the novel fusion estimator.