Research On Vehicle Test Technique In Validation Of INS Error Model

INS-error model validation test plays an important role in the manufacture of INS. Along with the conception of Point-Target-Clear, the study of INS error model validation test technique becomes more and more popular, and vehicle test technique is brought up in this background. Vehicle test is the vital segment in the validation of error model, precision and reliability.This thesis does intensive rsearches on the vehicle validation test method of SINS instrument error model which has 33 coefficients.Firstly, the separation model of guidance instrumentation systematic error is built using environment function matrix method, and the calculation of environment function matrix is introduced. The linearization error of the separation model increases very rapidly, when the time of test becomes long (about 1 hour). Taking account of this characteristic, two methods are brought forward to decrease the linearization error, they are respectively named "Expurgation-method" and "Repair-iterative-method" in this thesis. Then the outside measure scheme is designed using GPS; the 6-Ds dynamic model of the vehicle motion is built and the vehicle tracks of "0" type, "1" type, and "8" type have been simulated. In order to inspirit all errors of the guidance instrumentation and reduce the multicollinearity of environment function matrix, the postures of SINS are analyzed, which should be kept against the reference frame of vehicle system in the test. In this thesis, several estimate methods and criterions for the appraisal of results are also introduced. At last, the influence of initial aiming error on test results are analyzed and dealt with.The results of simulation indicate that: 21 parameters can be separated effectively in one test by least square method, through the experiment vehicle speed control strategy and the rules which the posture angles of SINS should change according to. There are 12 installation error parameters can not be separated effectively, as they have strong pertinence with the initial error. And the simulation results show that the correlativity only has relations with the initial posture of SINS in NRE reference frame. The 12 installation error parameters can be estimated more accurately when the tests are done for many times and the initial error characteristic is known.