A GPS Positioning Algorithm Based on Distributed Kalman Filter Data Fusion with Feedback

In order to solve the problem of larger single-point GPS positioning errors, a GPS positioning algorithm based on distributed Kalman filter data fusion with feedback (DKFDFWF) is proposed. Firstly, the data of the GPS receiver are filtered locally to obtain the local optimal state estimation and corresponding estimation error covariance. Secondly, the outputs of all local filters are fused in the fusion center to obtain the global fused state estimation and corresponding estimation error covariance. Finally, the output of the fusion center is fed back to each local filter to correct the larger estimation error of the abnormal local filter. We use three Unicore Communications UM220 receivers to locate and collect data in the same place, and continuously collect data for 24 hours. The experimental results show that the average maximum positioning error of distributed Kalman filter with feedback is reduced by 22.96% and 48.59% compared with single-point GPS Kalman filter and the averaging method of the position data of three GPS receivers, respectively. In other words, the proposed GPS positioning algorithm based on DKFDFWF has better positioning accuracy in the sense of weighted fusion than single-point GPS Kalman filter and the averaging method of the position data of three GPS receivers. The validity and feasibility of the proposed algorithm are verified.