论文信息 - Estimation of Noisy Data: The Case of Partially Missing Information

Estimation of Noisy Data: The Case of Partially Missing Information

Abstract We wish to estimate each random vector x ω ( t ) in the set K x 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.

Anatoli Torokhti | Phil Howlett

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