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.