Extended Kalman filtering using wireless sensor networks

Abstract

Wireless sensor networks are useful for many reasons, but they add at least two new issues to the Extended Kalman Filtering problem. First, they can be a further cause of divergence, as the information they send could not reach the filter. Second, batteries consumption must be taken into account: this leads to the need for a policy of querying, at each time instant, only a few sensors. In this paper we show how a wise sensor querying can improve the convergence rate of the filter, thus facing both the above problems. The querying criterion we suggest is simple to be implemented and adds a little computational overload to the filtering algorithm. The simulations we report, which refer to a mobile robot position estimation problem, show that it is effective in reducing the divergence rate of the filter.