Optimum linear estimation for random processes as the limit of estimates based on sampled data.

by Peter Swerling

Purchase Print Copy

 FormatList Price Price
Add to Cart Paperback24 pages $15.00 $12.00 20% Web Discount

An analysis of a generalized form of the problem of optimum linear filtering and prediction for random processes. It is shown that, under very general conditions, the optimum linear estimation based on the received signal, observed continuously for a finite interval <>, is the limit of optimum linear estimation in cases where the conventional generalized Wiener-Hopf integral equation technique has not been shown to yield a solution.

This report is part of the RAND Corporation paper series. The paper was a product of the RAND Corporation from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of individual authors to their professional fields. Papers were less formal than reports and did not require rigorous peer review.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.