The computing load of the inverse problem of an ordinary[5] Kalman recursion is roughly proportional to the cube of the number of the measurements processed simultaneously.
Any continued use of a too narrow window of input data weakens observability of the calibration parameters and, in the long run, this may lead to serious controllability problems totally unacceptable in safety-critical applications.
In problems of Satellite Geodesy,[6] the computing load of the HWB (and FKF) method is roughly proportional to the square of the total number of the state and calibration parameters only and not of the measurements that are billions.
However, these latter methods may solve the large matrix of all the error variances and covariances only approximately and the data fusion would not be performed in a strictly optimal fashion.
Such a large matrix can thus be most effectively inverted in a blockwise manner by using the following analytic inversion formula: of Frobenius where This is the FKF method that may make it computationally possible to estimate a much larger number of state and calibration parameters than an ordinary Kalman recursion can do.
[9] The FKF method extends the very high accuracies of Satellite Geodesy to Virtual Reference Station (VRS) Real Time Kinematic (RTK) surveying, mobile positioning and ultra-reliable navigation.
Therefore, improved built-in calibration and data communication infrastructures need to be developed first and introduced to public use before personal gadgets and machine-to-machine devices can make the best out of FKF.