Signal estimation over short data records : a data-dependent time-invariant algorithm

Document Type

Article

Date of Original Version

1-1-1990

Abstract

A new algorithm for the estimation of stationary random signals from short data records consisting of signal plus noise is given. Time-invariant algorithms to solve this problem suffer from transient effects at the beginning and end of the data record, while optimal algorithms are time-varying and thus computationally expensive. The approach presented here consists of a time-invariant Kalman smoothing estimator modified by a data-dependent correction term involving an estimated initial state vector. A theoretical performance analysis of the algorithm is presented, and comparisons are made with the optimal Kalman smoother, as well as with a different approach based on Wiener smoothing. The results show that the proposed approach has nearly optimal performance and is computationally efficient. © 1990.

Publication Title, e.g., Journal

Journal of the Franklin Institute

Volume

327

Issue

3

Link to Full Text

Share

COinS