Multistage robust filtering for linear systems

A multistage robust filtering procedure is developed for a linear, discrete, stochastic system where state noise and initial state are assumed to be zero-mean Gaussian and partial measurement covariance norm is bounded from below. Using minimax approach, it is shown that the least favorable measurement density is multivariable patched Gaussian and the best estimate is multi-dimensional soft limiter.