ANALYSYS OF METHODS FOR INCREASING THE METROLOGICAL CHARACTERISTICS IN TEST BENCHES FOR ANGLES AND ANGULAR VELOCITY VALUES ASSIGNMENT
This paper investigates the error estimation given by the assigned angular velocity in test benches which are based on the angle sensor, the angular velocity, tangential and centripetal accelerations sensors. Test benches are used in the production and research of angular rate sensors, units and systems based on them. The aim is to investigate the possibilities of increasing the accuracy of the angular velocity given by test benches due to the aggregation of information about angular velocity sensors of different physical nature. To achieve the given goals it is necessary to study the laws of distribution of probability density in the used sensors errors. We describe the design of the test bench, including the angle sensor, the angular velocity of the tangential and centripetal accelerations. Given are the expressions for the evaluation of the angular velocity according to data from each sensor. Showed is the mismatch between the error distribution law in different sensors which are part of the test bench and the normal distribution law. Moreover, for the angular velocity, the acceleration and the tangential centripetal acceleration sensors the law of density distribution in measurement error probability can be described using the Laplace integral, and for the angular sensor - using the poly-Gaussian distribution. The possible algorithm for identification the poly-Gaussian approximation parameter is described. Made are the conclusions on impossibility of applying the method of least squares to find the best estimation of the angular velocity and the feasibility of method of maximum likelihood. Obtained are the expressions for error distributions in the angular velocity as well as the tangential and centripetal acceleration sensors. The resultant errors are considered as the sum of systematic error having the uniform distributions in the fixed range and the random error being distributed in accordance with the Gaussian law with zero mathematical expectation and unknown variance. The probability density distribution for the angular sensor was given basing on the experimental data. This distribution is approximated by a superposition of some Gaussian distributions. The analytical expressions for used poly-Gaussian models are given. The validity of this approximation is proved using the Pearson criterion.