Research On Key Technology Of Low-cost Strapdown Inertial Navigation Systems For Aerial Guided Munition

The aerial munitions guided by global position system (GPS) and inertial navigation system (INS) have been widely used in modern battles for their low-cost and high accuracy. However, the signal of GPS is easily disturbed and the munitions must have the capacity to maintain the homing accuracy just based on the guidance of INS. The low-cost strapdown inertial navigation system (SINS), which is an important subsystem of guidance assembly, is studied in this dissertation. The key technology of that involved include low-cost hardware design, calibration and compensation of inertial measurement unit (IMU), nonlinear filter and transfer alignment.In hardware aspect, an integrative missile-borne computer is designed using DSP + FPGA structure. The low-cost flex gyroscopes and quartz flex accelerometers are chosen for the IMU based on the anticipative precision which was achieved by the analysis of error covariance. Low-cost and highspeed oversample A/D converter is designed for the IMU. This converter can provide 19-bit accuracy at data rate up to 200Hz.A novel method of calibrating and compensating gyros dynamic error (scale factor error and misalignment error) based on the coning error is proposed. When the coning motion of the shaking apparatus occurs, the coning error induced by gyros dynamic error may cause attitude drifts. Then the gyros dynamic error can be estimated and compensated by measuring the attitude drifts. The results of the simulation experiments show that this compensation method can reduce the attitude error caused by coning error by 90 percent.A method of calibrating and compensating the asymmetry of gyros dynamic errors based on neural network (NN) is presented. The angular rate of gyros output and compensation of gyros dynamic error are the input and output of the neural network respectively. In calibration test, the shaking apparatus was required to do single-axis shake from static, and then stop at the initial position. The terminal attitude drifts were used as the network performance function to train NN. Unlike the supervised training, the terminal attitude drifts were not the target outputs of NN. In this condition, the particle swarm optimization (PSO) algorithm was applied to train the network. The simulation experiment results demonstrate that the asymmetry of gyros dynamic errors reduce to about ten percent of those without the NN compensating. By using the low-cost SINS we designed, the shaking tests of different amplitude are performed. The mean attitude drifts after compensated is less than 0.8°/h.As low-cost IMU is sensitive to temperature, a neural network is designed to compensate the influence of temperature. Temperature measurement is used to be the input of NN. In static condition, the bias of the IMU, which is varied by different temperature and can be gotten by filtering the outputs of IMU, extracting and minus the initial value, is used as the anticipative output to train the NN. The results of the experiments show that the bias of IMU can reduce by 60% compared with those without compensation.The attitude errors of low-cost SINS may be too large to meet the hypothesis of linear model in initial alignment. A nonlinear model for large angle error was deduced to solve this problem. The Euler angles were introduced to present the attitude errors. To achieve accuracy propagation of SINS error model, none little attitude error hypothesis is made. Based on the large misalignment angle model, the extended Kalman filter (EKF) and the sigma-point Kalman filter (SPKF) are designed. Singular value decomposition (SVD) is used to improve SPKF, in the condition that the updating covariance matrix is negative. The simulation experiments results demonstrate that the filter based on the large misalignment angle model has better accuracy than any other traditional filters based on linear model or large heading uncertainty model, under large attitude error of initial alignment.The large misalignment angle model was used in rapid transfer alignment. The SPKF was applied to avoid the derivation calculus of the attitude measurement equations which were with complex nonlinear characteristic. The simulation platform is designed to compare the alignment accuracy of the nonlinear filter based on the large misalignment angle model and conventional Kalman filter. The results of simulation experiments show that the alignment accuracy achieved by the nonlinear Kalman filter base on the large misalignment angle model is higher than that of linear Kalman filter. In large misalignment error condition, the heading alignment accuracy achieved by the nonlinear filter is 10 times higher than that of Kalman filter. The nonlinear filter is not sensitive to the lever arm error and gyros dynamic error. The results of mobile tests validated the alignment algorithm. Five tests results revealed that the position errors were limited up to 10m compared to 40m before alignment.