Data fusion of integrated navigation system based on confidence weighted

A novel method on the data fusion framework is presented in this paper, taking the confidence of sub-filters into consideration. For the standard Kalman filter theory, the confidence of sub-filters is degraded by the time varied statistic of measurement noise, meanwhile the federated Kalman filter integrating these sub-filters is severely influenced. To cope with that, firstly, the fuzzy adaptive Kalman filter is applied to sub-filters to make them work optimally. Secondly, the confident function is used to compute the confidence of sub-filters, then the confidence of federated filter is calculated based on the confidence of sub-filters. And then the results of sub-filters and federated filter are weighted with the corresponding confidences. Simulations in INS/GPS/Odometer integrated navigation system demonstrate that the weight of sub-filter with low confidence is limited, and that the precision is improved as compared with the standard federated Kalman filter.