Extended Kalman filtering for shadow/fading power estimation in mobile communications

The objective of this research is to apply the Extended Kalman Filter and Nonlinear Control Theory to improve local mean power estimation in a mobile wireless communication system.;This research contributes the following:;1) Application of a new Extended Kalman filter (NEKF) approach to improve local mean power estimation. The method is being validated using the Matlab/Simulink/GUI system model and was compared to existing methods, Kalman Filter (KF), in Gaussian and Non-Gaussian noise environments. Our analysis and experiments demonstrate that EKF is a more accurate method.;2) Development of an accurate estimation of parameters and higher order state space prediction for modeling shadow power. Statistical methods for parameter estimation of linear models in dynamic mobile communication systems have been developed.;3) Development of an algorithm introducing a discrete-time approach based on pilot signal strength measurements. Exact analytical expressions are developed evaluating path loss performance metrics for a mobile station moving along a straight-line trajectory in a mobile network.