Research On The Key Technology Of Strapdown Guidance System

Modern local wars put forward a development request of precision, miniaturization, and low cost to guided wespons. Seeker is the core component of precision-guided weapons, which largely determines the accuracy and cost of guided weapons.With the rapid development of high-resolution large-array detectors and MEMS Strapdown Inertial Navigation System(SINS), strapdown navigation and guidance technology has become an important research topic of guidance field. Foreign reference research results have been applied to tactical weapons.This paper focuses on decoupling and estimation algorithm, and error sensitivity analysis of line-of-sight(LOS) rate in the strapdown guidance system, and key technologies such as MEMS SINS laboratory calibration and transfer alignment calibration online. Finally, the correctness and accuracy of LOS rate estimation algorithm is verified through semi-physical simulation experiment.In order to research on the key technology of strapdown guidance system, the strapdown guidance system is modeled, and involved coordinate system and the relationships between different coordinates are given. The mathematical model of the strapdown seeker and its linearization method are researched. To remove the body LOS(BLOS) angle which is contained in the missile motion information, Mathematics platform is built to research LOS rate decoupling algorithm. Using the error theory, the error sensitivity of BLOS angle, BLOS angle rate, body angular rate and attitude angle with respect to LOS rate are derived respectively. The main factors of affecting LOS rate accuracy are obtained by the simulation analysis. On this basis, the causes and offset correction method of these error is detail analized.In tne process of researching MEMS SINS key technology, MEMS SINS mechanical arrangement and error equation are derived, and the overall calibration and compensation algorithm is proposed. and the systematic error model of MEMS Inertial Measurement Unit(MEMS IMU) is established, after the compensation, the bias of MEMS IMU have been improved to 0.20°/s and 6.5mg from 1.27°/s and 145.0mg, the using accuracy is highly improved. For the problem of MEMS SINS accuracy having a great impact on the LOS rate accuracy, and the unknown and time-varying characteristics of the process noise and measurement noise covariance in the practical application, the mechanical layout equation and error model of MEMS SINS are derived, the "Velocity + Attitude" matching transfer alignment algorithm based on an improved adaptive delta Kalman filter is proposed, which can calibrate and correct MEMS IMU random bias and initial attitude angle value online. Simulation results show that this algorithm estimation accuracy is increased more than 5 times than the standard Kalman filter accuracy, and the alignment rapidity is improved, which provides an important theoretical basis for strapdown guidance system applications.On the basis of effectively improving the accuracy of BLOS angle, body angular rate, and attitude angle accuracy, the LOS rate estimation nonlinear model is built according to missile-target relative kinematics and body attitude kinematics. Depending on the measurement information of strapdown seeker and MEMS SINS, the measurement model is established, using unscented Kalman filter(UKF) to estimate LOS rate. Its estimation accuracy is 0.21°/s, which can meet the guidance system accuracy requirements. In addition, analysis shows that the LOS angle and rate accuracy have an nearly linear relationship with gyro accuracy, and the scale factors are approximately 0.0012 °/°/h and 0.0002 °/s/°/h.Finally, the semi-physical simulation experiment is designed to verify of the above work, the estimation error of gyro bias and accelerometer bias are less than 0.5°/h and 1mg respectively, the convergence time is less than 3s. LOS rate estimation accuracy is about 0.16°/s. The semi-physical simulation results are in good agreement with the numerical simulation, which provides theoretical support for the estimation algorithm applied to practical engineering。...