Research on GPSINS Full-Depth Integrated Navigation System Based on H

< p> GPS / INS combination methods include position and speed combination, pseudorange and pseudorange rate combination, etc. These combination methods are basically the combination of position and speed information, without directly observing the azimuth and attitude information, so the carrier does not do When maneuvering, its orientation is prone to divergence. In some systems that require high attitude and orientation, such as aircraft aerial alignment, aerial refueling, aircraft approach and landing, etc., the above combined systems are not competent. Multi-antenna GPS can measure the attitude and orientation of the carrier, so it can combine the observation information of attitude and orientation. This is an effective way to overcome the above phenomenon. The most commonly used algorithm for combining GPSNS is Kalman filter, and Kalman filter has only The best estimate can be obtained only when the system model and the statistical characteristics of noise are known accurately. However, there are various uncertainties in the actual GPS and INS systems. It is difficult to accurately model these uncertain noises, which makes the Kalman filter estimation accuracy greatly reduce in actual work, and the filter divergence will occur in severe cases; After the introduction of attitude and bearing observation information, the complexity of the system is further increased, making it difficult to guarantee the real-time navigation. Robust filtering developed in recent years is very robust to the uncertainty of system noise. There is no need to accurately model interference noise, which is beneficial to simplify the system structure.

Therefore, the application of robust filtering technology to the GPSNS integrated navigation system is of great significance to ensure the navigation accuracy of the integrated system, improve the reliability and real-time performance of the system, and prevent the filter from diverging. 2 Robust filtering generally, for linear time-varying discrete For the system, the state equation and the observation equation are the discrete form of the weighted matrix of noise; Ck is the discrete form of the observation matrix of the system; Dk is the discrete form of the weighted array of observation noise; m, vk are the system noise and the observation noise respectively; k = 0, 1, 2, ..., N If m, vk is a finite energy signal in all time intervals, 0kSN is a finite energy signal, that is, robust filtering is to find a filter. For a given minimum V, satisfying Kk = T is the system state variable, (- N, O, O is the platform error as speed error Elol is generally s jail error ft; eA is the system transmission yd; tolerance is 1: for gyro noise interference, Wax, Way, Woz is accelerometer noise interference equation (8 ) Discretization, which can be converted into the discrete state equation form of (1) (2) Combined system observation equation There are many GPSNS combination methods. The combination methods studied in this paper are GPSNS pseudorange, pseudorange rate and carrier phase Deep combination, so the observation equation is the observation matrix of pseudorange rate and double-difference carrier phase; Vp, Vp, VG are the uncertainty of observation of pseudorange, pseudorange rate and carrier phase, respectively. Uncertainty noise has strong robustness, and it is also unnecessary to consider its precise error characteristics. The combined equations (9), (10) and (11) give the observation equation of the combined system as the observation matrix. 4 The integrated navigation system simulation and results are Reflecting the generality, this paper carried out a dynamic simulation of GPS / INS full depth combination. The simulated trajectory includes the processes of rolling, taking off, climbing, cruising, turning and diving. The initial position is selected as longitude 118 Â° east, latitude 32 Â° north, altitude It is 1000m, the initial heading angle 45 * level flight speed is 300m / s, the simulation time is 2000s and it is assumed that the error characteristics of the three gyroscopes and the three accelerometers are the same, and they are all set to colored noise, where the random constant drift of the gyroscope is 0. 1 (*) / h, first-order Markov drift 0.1 (* / h, first-order Markov correlation time is 1000s, white noise is Q01 (* / h; accelerometer zero bias is 1CT4g, accelerometer first-order Markov Kov process is 10-4g, first-order horse The Kelkov correlation time is 1000s; the initial error angle of the platform is taken as 100 "north, 100" east, 200 "ground, initial position error 50m, initial velocity error 0. Measurement error is offset 10m, random 30m; random pseudo The distance rate error is 0.05m / s, and the carrier phase measurement error is 0.0019m. For comparison and analysis, in this paper, the simulation of the combination of pseudorange and pseudorange rate using filtering in the presence of the above colored noise and Kalman filtering and filtering respectively Simulation of the combination of pseudorange, pseudorange rate, and carrier phase full depth Through system simulation, the simulation curves of each state of the system flying under various conditions can be obtained as shown in ~. During the simulation process, in order to take into account the observation of colored noise in the system The robustness of the colored noise and the accuracy of the system state estimation value, after multiple simulation adjustments, determine the V value to be Q5. From the simulation results (and), this value is suitable. To further improve the estimation accuracy, the V value It can be larger, but because the system noise and observation noise used in this paper are strong colored noise, its robustness is the main factor, so the value of this paper is low. It can be seen that the application of filtering in colored noise When present pseudorange pseudorange rate converges integrated navigation system, but the accuracy of the time difference than the whole composition, and the absence of direct observation attitude and orientation, azimuth vectors do not converge before when maneuvering at brother. (See the figure below and use ", the phase combination of the attitude and the observation position of the * position". After the GPS / INS pseudo-range and pseudo-range rate combined filter curve under colored noise, the azimuth angle converges well throughout the process (see ), And the accuracy of attitude, position and speed has been further improved. The estimated accuracy of the steady-state value is shown in Table 1. Comparison and it can be seen that when the full combination is performed with ordinary Kalman filters in the presence of colored noise, the system diverges and cannot be performed normally. Navigation, and the application is good = all states of the fully integrated system of the filter can converge well and maintain a high accuracy. Table 1 shows the standard deviation of the navigation parameter error (le) classification position of the filtered GPS / INS full-deep integrated navigation system / m Rolling pitch course heading north to east ground longitude latitude longitude standard deviation 5 Conclusion By applying filtering to the dynamic simulation of the entire track of the GPS / INS full-deep integrated navigation system and analyzing the error state curve, the following conclusions can be obtained: The application of Le filter technology to the GPS / INS pseudorange, pseudorange rate and carrier phase combined navigation system is successful, and can be used in dynamic systems with strong colored noise; Le filter Compared with the Kalman filter, the filter is very robust. Even if the input signal contains strong colored noise, the filter can still converge, and it can maintain a very precise accuracy; when only using the combination of pseudorange and pseudorange rate, the azimuth angle When the carrier is not maneuvering, the sword will not be closed, but the full combination of pseudorange, pseudorange rate and carrier phase can eliminate this phenomenon, and at the same time can improve the estimation accuracy of the entire system; use: 'filter for GPS / INS combined navigation, The system state equation can only include position error and speed, which is conducive to the real-time calculation of the navigation system. In summary, this paper applies the robust filtering theory to the GPS / INS pseudorange, pseudorange rate and carrier phase full-deep integrated navigation system for the first time. A filtering-based GPS / INS pseudorange, pseudorange rate and carrier phase fully integrated navigation system scheme is proposed, and a dynamic simulation is carried out. The results show that the scheme eliminates the phenomenon that the azimuth angle does not converge when the carrier is not maneuvering. Greatly improve the robustness and reliability of the system, improve the navigation accuracy of the entire system, and at the same time reduce the complexity of the system, which is of great significance to the practical application of engineering

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