Research On Technology Of Kalman Filtering In Navigation Receiver Under The Condition That Carrier Mobility

Navigation Filtering technologies as an important link in back-end processing technology, its performance is decided by the observation equation and motion equation together. In maneuvering target, target's motion state is multiply, and when it effected by the environment, navigation signal become weak and may be seriously shielded. Then, the problem that navigation measurements' quality decline and estimation of the maneuver station fully exposed,this will directly lead to the decline of the positioning accuracy of the navigation receiver. In view of the above problems, around the demand of user for the positioning robustness under the maneuvering conditions. The paper from characteristic analysis and suppression of the Beidou measurements' error ?multiple model filtering solution and Doppler assisted positioning. The concrete research contents are as follows:The first part, according to the problem that the multipath errors and ionosphere error influenced by the environment changing and mainly effect the positioning accuracy of the navigation receiver. Analysis the multipath error changing characteristics of GEO?IGSO?MEO satellite in one week by using the measured data of the Beidou satellite in orbit. To provide the reference for the multipath error modeling. Analysis the accuracy of different ionospheric correction method in the different times of one day, and analysis the influence of the positioning accuracy by different ionospheric delay error methods. Give reference for single frequency navigation receiver processing ionospheric delay error.The second part, in view of the problem that tracking performance is poor in the conversion of maneuvering by traditional interactive multi model algorithm. The improved method bring strong tracking filter into traditional algorithm, it can improve the motor conversion time filtering robustness. The result of simulation trial show that compared with the traditional interacting multiple model algorithm, the improved method can make the horizontal direction error variance from 18.11 m to 13.39 m. Analysis shows that when using the real measured data, the improved method can reduce the level of error variance from 10.89 m to 7.86 m.The third part, in view the problem that traditional Doppler aided algorithm only assist to estimation in velocity dimension. Under maneuver condition it may be poor estimated. Proposes a kalman filtering algorithm base on the fusion of Doppler and pseudorange. the results of simulation trial show that compared with the traditional Doppler positioning algorithm, the improved method can make the horizontal error decreased from 3.43 m to 1.56 m, Analysis shows that when using the real measured data, the improved method can reduce the level of error variance from 56.72 m to 39.50 m.Finally, the thesis research and engineering applications are summarized, and the next step forthcoming work were discussed.