Study On Key Technology Of High Precision Vehicle Inertial Navigation System

In this dissertation, the key technology that can upgrade the navigation accuracy of vehicle inertial navigation system is studied. The observation and compensation schemes of drift of platform inertial navigation system are studied by using genetic algorithm (GA), Kalman filter and rotating accelerometer gravity gradiometer.First, the relevant problems with GA are studied, a float-coded genetic algorithm (FGA) is designed, it has advantages of fast convergence, high accuracy to find the best solution and ease for implementation. The FGA's effectiveness is further demonstrated by looking for the optimal solution for several benchmark GA test functions. It is also demonstrated that on finding the best solution for real-value function, the FGA is better than binary-coded genetic algorithm. The convergence of the designed FGA is proved by using limited Markov chain. A self-compensation algorithm of platform drift with FGA is proposed. The reasons that lead to the platform drift are analyzed in platform inertial navigation system. These time-variant disturbance torques which lead to platform drift are detailedly classified and described by model parameters of disturbance torques, the function relation from model parameters to output signal of gyroscope is established, these model parameters are identified by using FGA for compensating disturbance torques, further for decreasing platform drift. This algorithm is a self-compensation scheme of platform drift because it only use output signal of gyroscope in platform. The simulation result demonstrated efficiency of this method.In navigation field, Kalman filter is used in parameters estimation and integrated navigation. A Kalman filter is designed by...