Estimation of Noisy Data: The Case of Partially Missing Information

We wish to estimate each random vector xω(t) in the set Kx from the corresponding large set of noisy observations. The conceptual foundation of the proposed filter is an optimal least squares linear estimate of the incremental change to the p signal pairs, extended by a natural interpolation to an estimated value of each reference signal.