Abstract:Gross error cannot be avoided as observations will be affected by environmental and some uncertain factors. In this paper, we introduce two adaptive factors based on a robust Kalman filter to adjust imprecise dynamic models and observation models which interfuse gross error. According to the low efficiency of the robust adaptive Kalman filter, we construct achi-squared distribution based on a robust Kalman filter. We test gross error by chi-squared distribution, then use a robust adaptive Kalman filter to process data in these algorithms.Experimental results show that the algorithm of dual adaptive factors filtering can resist gross error efficiently and weaken adverse effects due to the imprecise dynamic model. A robust Kalman filter based on chi-squared distribution can resist the effects of gross error and the convergence rate will also be improved.