Second-order polynomial estimators from uncertain observations using covariance information
نویسندگان
چکیده
This paper presents recursive least mean-squared error second-order polynomial filtering and fixed-point smoothing algorithms to estimate a signal, from uncertain observations, when only the information on the moments up to fourth-order of the signal and observation noise is available. The estimators require the autocovariance and crosscovariance functions of the signal and their second-order powers in a semidegenerate kernel form, and the probability that the signal exists in the observed values. 2002 Elsevier Science Inc. All rights reserved.
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عنوان ژورنال:
- Applied Mathematics and Computation
دوره 143 شماره
صفحات -
تاریخ انتشار 2003