Stochastic modeling of pseudolite clock errors using enhanced AR methods

In a pseudolites based real-time locating system, autoregressive (AR) techniques are applied to model the stochastic behavior of the pseudolite clocks errors. A stable model to predict the clock deviations is obtained from the AR parameters of a process smoothed by a Kalman filter. A performance comparison of the proposed scheme with an approach based on a Markov model is performed using measurements from a real-world experiment. It is shown that the proposed scheme can predict the clock deviations with an accuracy of picoseconds leading to location errors of few centimeters.