Improved Estimation of the Number of Competing Stations Using Scaled Unscented Filter in an IEEE 802.11 Network

Abstract

In this paper, a new methodology to estimate the number of competing stations in an IEEE 802.11 network, is proposed. Due to the nonlinear nature of the measurement model, an iterative nonlinear filtering algorithm. called the Scaled Unscented Filter (SUF), is employed. The SUF can provide a superior alternative to nonlinear filtering than the conventional Extended Kalman Filter (EKE), since it avoids errors associated with linearization. This approach demonstrates both high accuracy in addition to prompt reactivity to changes in the network occupancy status. In particular, the proposed algorithm shows superior performance in non saturated conditions when compared to the EKF. Numerical results demonstrate that the proposed algorithm provides a more viable method for estimation of the number of competing stations in an IEEE 802.11 network. than estimators based on the EKE