The aim of the addressed filtering problem is to design a filter such that, in the presence of both the stochastic nonlinearities and multiple missing measurements, there exists an upper bound for the filtering error covariance.
Completely missing measurements will be handled using multiple imputation.
In addition, the tracker will be able to handle some missing measurements for each pore (ilost).
Missing data will be replaced by an imputation method for missing measurements.
The missing measurements appear in a random way which is governed by missing rates obeying a certain probability distribution.
Sensor saturations, missing measurements and channel fadings are governed by random variables obeying the Bernoulli distributions.
The systems under consideration are subjected to time delays, multiplicative noises and randomly occurring missing measurements.
The phenomena of ROQ and missing measurements are governed by a Bernoulli distributed stochastic sequence.
Only 25 days have missing measurements.
The missing measurements are considered the finite-time framework.
