Design And Identification Methods For Self-calibration Test Of Inertial Navigation Platform System

The calibration technology of system level is far different from that of a single instrument in the laboratory test. Some error coefficients of instruments tend to change as environmental conditions change, which need to be recalibrated. Inertial navigation platform system(INPS), which is based on inertial stabilized platform, contains a system of framework with three rotational degrees of freedom. The rotational function enables it to realize self-calibration not rely on external reference test equipment or reference. Continuous tumbling test method is compared with the multi-position testing method which is used broadly now, because the platform works always in servo condition, not only can make full use of all the observing information during the tumbling process and identify more error coefficients, but also has a higher calibrating accuracy and the testing process is simpler and more efficient. On the other hand, the error model equation and experimental design process for continuous tumbling test is more complicated.The research in this thesis is about the related problems in continuous tumbling self-calibration experimental design and identification methods of error model parameters for inertial navigation platform system. First, a total of 30 error model parameters of the three accelerometers and three gyroscopes are selected, state equations and observation equations are respectively established using Y angles and accelerometer outputs. In order to improve the observability degree of parameters to be identified and obtain optimal rotation trajectory of platform continuous tumbling experiment, using D optimal experimental design method to get the corresponding mathematical expression. By means of appropriate engineering simplification and mathematical deduction, the optimal design problem of continuous tumbling test is transformed into an optimal control problem to be solved.According to the solution of the optimal design of experiment, using the global intelligent optimization algorithm. At the same time in order to improve the computational efficiency and the precision of the optimal trajectory, Barrier function and the improved RSSA algorithm are introduced, which transforms the nonlinear constrained optimization problem into unconstrained optimization problem. The computing performance of new method is superior to the traditional genetic algorithm. The simulation results show that reasonable configuration for the algorithm parameters, not only greatly improves the efficiency of solving the D optimal design problem, but also the fitness values of optimal trajectory obtained is better than traditional genetic algorithm.Based on the results of the optimal trajectory design of the platform continuous tumbling experiment. The error identification method for continuous tumbling self-calibration test in the thesis is studied. On the basis of the established system model based on Y angles, we introduce the cosine transformational matrix. The innovation sequence for the accelerometer calibration is the vertical component of the acceleration error, and the innovation sequence for the gyro calibration is the horizontal component of the acceleration error. The identification method of dual kalman filter is proposed to separate gyroscope and accelerometer identification by decoupling them, we can verify the effectiveness of the double-kalman-filter identification method by the simulation results.