Abstract
This paper presents non-iterative cooperative/parallel Kalman filtering algorithms for decentralized network navigation, in which mobile nodes cooperate in both spatial and temporal domains to infer their positions. We begin by presenting an augmented minimum-mean-square error (MMSE) estimator for centralized navigation network, and then decouple it into a set of local sub-ones each corresponding to a mobile node; all these sub-estimators work in parallel and cooperatively—with the state estimates exchanging between neighbors—to provide results similar to those obtained by the augmented one. After that, we employ the approximation methods that adopted in the conventional nonlinear Kalman filters to calculate the second-order terms involved in these sub-estimators, and propose a decentralized cooperative/parallel Kalman filtering based network navigation framework. Finally, upon the framework, we present two cooperative/parallel Kalman filtering algorithms corresponding to the extended and unscented Kalman filters respectively, and compare them with conventional decentralized methods by simulations to show the superiority.
