EVALUATION OF MEASUREMENTS OF THE DISTANCE TO THE OBJECT IN THE STUDY OF ITS GRAPHIC IMAGE
An important element of modern automated systems of management, monitoring and control of remote access modules is information about the state and behavior of a static or moving object. In many existing and planned monitoring systems processing graphical image of the object is used, which is obtained by the photo detectors and, thus, the possibility of determining geometric and kinematic parameters of a moving object is significantly reduced due to various aspects of image acquisition, one of such aspects is blur. In the present work, algorithms of the primary information processing obtained on the basis of the graphic image study of a movable or stationary object are improved using the methods and procedures of statistical analysis that allow approximating theoretical results to experimental results. The use of statistical analysis and probabilistic approach increase the accuracy of the determined characteristics, applicability of calculation procedures of the state parameters (size, shape, distance from the observer) and behavior of the object (speed and direction) and reduce the computational complexity of the final algorithm. The Bayesian estimation was obtained based on the use of quadratic, rectangular and simple loss function under normal, Laplace, uniform and lognormal distribution of errors, which allow drawing conclusions about the intervals of various models and algorithms to determine the parameters of different objects. When using the statistical approach it is taken into account that the errors are random in nature and may be considered a variety of probability density functions (normal, lognormal, Laplace and uniform distributions to minimize risks under different loss functions (quadratic, rectangular, linear), and then evaluated using the method of least squares, method of least modules and the Bayesian approach. When performing the evaluation the properties of unbiased, consistency and efficiency are important. Sustainable procedure should have the following properties: for the selected model, the procedure should be close to optimum efficiency; the results should be close to nominal, calculated for the adopted model; the effect of large errors must be eliminated. We use the minimax method of Huber, which assumes that the best estimate will not be worse than in the case of the “least favorable” density distribution. Decision rule is based on the definition of such density that minimizes the information of Fischer that is the variance function of the contribution of the sample. In the present study we offer the procedure of finding a theoretical function based on the assumption that the errors are subjected to the known laws of distribution: normal (Gaussian), Laplacian, uniform, lognormal. This is a significant advantage of the proposed methodology compared to the one used in the previous works of the authors, it is proposed here to use the Bayesian estimation of the measurements as unknown theoretical function that needs to be obtained closer to the observational measurements.