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Kalman filtering of measurement data from multiple sensors with time-varying delays and missing measurements is considered in this work. Two existing approaches to Kalman filtering with delays are extended by removing some assumptions in order to have equivalent filtering methods and making comparisons between them. The computational loads of the two methods are compared in terms of the average number of floating point operations required by each method for different system dimensionalities and delay upper bounds. The results show that the superiority of the methods over each other depends on the comparison conditions.