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

Eyal Levy; Tirza Routtenberg

doi:https://doi.org/10.1016/j.dib.2020.106714

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

Eyal Levy, Tirza Routtenberg

Department of Electrical & Computer Engineering

Ben-Gurion University of the Negev

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	American English
Article number	106714
Journal	Data in Brief
Volume	34
DOIs	https://doi.org/10.1016/j.dib.2020.106714
State	Published - 1 Feb 2021

Keywords

Frequency deviation
Locally most powerful
Low-complexity methods
Nuisance parameters
Unbiased test

All Science Journal Classification (ASJC) codes

General

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https://doi.org/10.1016/j.dib.2020.106714

Cite this

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abstract = "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.",

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N2 - 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.

AB - 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.

KW - Frequency deviation

KW - Locally most powerful

KW - Low-complexity methods

KW - Nuisance parameters

KW - Unbiased test

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

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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