Abstract: BLUE(Best linear unbiased estimation) filter can be used for target tracking. Influenced by the colinearity between the slant range measurement and the altitude measurement, BLUE filter's estimation may degrade or diverge for close-range target tracking. To solve this problem, the biased weighted estimates of slant range and altitude were employed in the converted measurements to alleviate the colinearity. The bias and weighing of the slant range and altitude parameters were derived based on the minimum mean square error criteria. The converted measurement model with fusion of nonlinear measurement and state prediction was built. The improved BLUE algorithm was able to estimate the statistics of the converted measurements more accurately, hence the filtering accuracy was improved. Simulation results verified this model can greatly improve the performances with minor computational burden. It was also shown to have excellent robustness in numerical examples, which proved it to be a practical approach.