Optimal estimation of digital stochastic sequences
作者:
A. J. MILLER,
P. MARS,
期刊:
International Journal of Systems Science
(Taylor Available online 1977)
卷期:
Volume 8,
issue 6
页码: 683-696
ISSN:0020-7721
年代: 1977
DOI:10.1080/00207727708942074
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
The paper considers the problem of the optimal on-line estimation of digital stochastic sequences. Specifically a calculus of variations approach is used to prove that for stationary stochastic sequences exponential and moving average algorithms are equally viable, both techniques representing close approximations to the theoretical optimum solution. For non-stationary sequences the moving average algorithm is shown to be the better choice, due to its symmetrical weighting function. The paper concludes with a discussion of the influence of induced correlation on the estimation process.
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