Title
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
Year
DOI
Venue
2008
10.1093/ietcom/e91-b.11.3688
IEICE TRANSACTIONS ON COMMUNICATIONS
Keywords
Field
DocType
tracking and estimation, IEEE 802.11 wireless LAN, extended Kalman filter, scaled unscented filter
Extended Kalman filter,Nonlinear system,IEEE 802.11,Computer simulation,Simulation,Computer science,Iterative method,Nonlinear filtering,Algorithm,Linearization,Distributed computing,Estimator
Journal
Volume
Issue
ISSN
E91B
11
0916-8516
Citations 
PageRank 
References 
0
0.34
1
Authors
3
Name
Order
Citations
PageRank
Jang-Sub Kim113313.04
Ho-jin Shin2338.49
Dong Ryeol Shin3148.21