Research And Design Of Kalman Filter In High Dynamic GNSS Receiver

Along with the Satellite Navigation technology maturing gradually, the application requirements of the GNSS (Global Navigation Satellite System) in aerospace dynamic environment strengthen increasingly. In the high dynamic environment, satellite signal tracking ability of the receiver becomes poor, thus observation used for locating and velocity detecting contained the random error, which will lead to reduction of position and velocity measuring precision of the receiver. Using kalman filter technique can estimate the users'position and velocity state in high-precision from the observation including random error. Calculating receiver's position and velocity based on kalman filter needs to establish a reasonable system state model and observation model, and consider filter convergence rate, operation efficiency and filter divergence inhibition etc. In this paper, based on studying the basic principle of GNSS receiver positioning and key technologies of kalman filter, user receiver's state variable is modeled combining with practical statistical-current model while nonlinear observation model is established according to the relationship of system observation and state, and high dynamic user receiver's position and velocity real-time calculating is completed based on extended kalman filter. An enhanced method which can further improve precision of the receiver's position and velocity detecting is put forward, through improving the precision of the system observation by extracting high precision doppler from carrier doppler phase using kalman filter. Software realizing of the kalman filter positioning is introduced. Processing techniques are proposed to improve the convergence speed and operation efficiency of the calculating. Through the simulation and board level testing, the results show that receiver position and velocity calculating precision based on kalman filter is superior to the traditional least squares method, and proposed improved algorithm to advance observation precision based on kalman filter can enhance positioning and velocity precision further.