Recursive estimation of linear model parameters from several groups of measurements

Recursive algorithms are proposed for design of estimates of unknown linear model parameters from sevelar groups of measurements with incomplete data on statistical measurement errors. Conditions are described under which the proposed estimates are asymptotically equivalent in terms of probability to the best linear unbiased parameter estimates; strong credibility of estimates is proved for unknown variances of measurement errors. A numerical example is considered.