GPS fault detection and exclusion using moving average filters

A new approach based on the moving average (MA) is proposed to perform satellite failure detection,and exclusion (FIDE). By taking the average of the last few sums of the squares of the GPS range residual errors, the MA filter is applied to speed up failure detection. The detection threshold cannot be obtained directly because the cumulative distribution of a random process with an MA filter is unknown. Therefore, the Markov chain approach is applied to resolve the threshold. In addition, variations in the number of visible satellites may cause problems in data fusion. The probability integral transformation (PIT) method is adopted to overcome this. After a satellite failure is detected, the multivariate MA filter is used to reduce the incorrect exclusion rate (IER) by taking the average of the last few parity vectors. Simulation results show that, in comparison with the conventional least-squares-residuals method, the MA filter demonstrates higher performance in detecting small failures and a similar level of performance in detecting large failures. Moreover, simulation results also verify that the proposed method has lower IER than the conventional parity space method.