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 (EKF), 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 EKF.

In this paper, a multiuser receiver based on a Gaussian Mixture Sigma Point Particle Filter (GMSPPF), which can be used for joint channel coefficient estimation and time delay tracking in CDMA communication systems, is introduced. The proposed algorithm has better improved estimation performance than either Extended Kalman Filter (EKF) or Particle Filter (PF). The Cramer-Rao Lower Bound (CRLB) is derived for the estimator, and the simulation result demonstrates that it is almost completely near-far resistant. For this reason, it is believed that the proposed estimator can replace well-known filters such as the EKF or PF.