Supplemental data for the paper “low-complexity detection of small frequency deviations by the generalized LMPU test”

Supplemental data for the paper “low-complexity detection of small frequency deviations by the generalized LMPU test”

This document contains supplemental material for the paper [2]. The notations in this document are the same as in [2]. In particular, we first present here the proof of Theorem 1 in [2]. This theorem expresses the locally most powerful unbiased (LMPU) test, which is a general method for local detect...

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Bibliographic Details
Main Authors: Eyal Levy, Tirza Routtenberg
Format: Article
Language:English
Published: Elsevier 2021-02-01
Series:Data in Brief
Subjects:
Locally most powerful
Unbiased test
Nuisance parameters
Low-complexity methods
Frequency deviation
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340920315936
Description
Summary:This document contains supplemental material for the paper [2]. The notations in this document are the same as in [2]. In particular, we first present here the proof of Theorem 1 in [2]. This theorem expresses the locally most powerful unbiased (LMPU) test, which is a general method for local detection, in the presence of known nuisance parameters. Second, we present here the Matlab code of the LMPU and the generalized LMPU for the special case of detection of a small deviation in the frequency of sinusoidal signals, which arises in various signal processing applications.
ISSN:2352-3409

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