Influence of Errors on the Accuracy of the Spatial Monitoring Results Under Redundant Information

The presence of information redundancy allows to obtain an overall estimate by various relatively simple measuring devices using minimally sufficient set of fundamental measurements. Herewith, estimated parameters tend to be related to the measured initial estimates of nonlinear functional relationships. Therefore, direct use of the maximum likelihood method leads to the necessity of solving systems of nonlinear equations. Applying the linearization method of nonlinear functional relationships allows to obtain optimal (in this case maximum likely) estimates of the final parameter and the correlation matrix of estimation errors in an explicit form. Herewith, solving the problem of optimal use of estimates of the same state vector obtained by different methods simultaneously, is reduced to a consistent application of the estimate filtering algorithm. However, the weight matrix, included in the expression for determining the overall estimate depends on the values of the measured parameter and is not always known a priori. Based on this, without dwelling on the possible ways of obtaining the weight matrix, the analysis of the influence of its determination accuracy on the overall estimate accuracy was performed. It is shown that the elements of the correlation error matrix of the overall parameter estimate depend not only on the accuracy of the initial estimates, obtained from different measuring devices simultaneously, but also on the estimation accuracy of the weight matrix, methods of its determination and the closure error of initial parameter estimates