Distributed cubature H-infinity information filtering for target tracking against uncertain noise statistics

Abstract

The traditional Kalman-based distributed state estimation (DSE) is unable to address uncertain system noise. To this end, this paper studies robust DSE problems for nonlinear systems over wireless sensor networks (WSNs) with uncertain noise. The distributed cubature H-infinity information filter (DCHIF) is developed by exploiting the weighted average consensus technique. The DCHIF is employed for high-dimensional state estimation with uncertain system noise in WSNs. The local estimation in the DCHIF is obtained by each single sensor without being affected by the failure of non-adjacent sensors. The estimation is then transmitted to the adjacent fusion centres for information fusion according to the weighted average consensus (WAC) technique. The DCHIF has advantages, such as high precision and strong robustness that can alleviate the influence of uncertain noise statistics on the state estimation. Therefore, to guarantee the stability regardless of the number of consensus steps, a novel convergence analysis approach is presented based on innovative terms for each sensor node. Additionally, the efficiency of the proposed algorithm is demonstrated by performing a numerical simulation of a typical air-traffic control system.