Analysis of sensor impulse response effects on Cramèr–Rao lower bounds for signal parameter estimators

In this study, a generic analysis of sensor impulse response effects on linearly filtered channel noise is presented to illustrate the resulting variations to the Cramèr–Rao lower bounds (CRLBs) of signal parameter estimators in signal processing and communication applications. The authors start by...

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Main Authors: Ephraim S. Gower, Malcom O.J. Hawksford
Format: Article
Language:English
Published: Wiley 2014-12-01
Series:The Journal of Engineering
Subjects:
Online Access:http://digital-library.theiet.org/content/journals/10.1049/joe.2014.0230
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spelling doaj-1e0ddab1c663449cb0486c95174a94282021-04-02T15:42:20ZengWileyThe Journal of Engineering2051-33052014-12-0110.1049/joe.2014.0230JOE.2014.0230Analysis of sensor impulse response effects on Cramèr–Rao lower bounds for signal parameter estimatorsEphraim S. Gower0Malcom O.J. Hawksford1Botswana Institute for Technology Research and InnovationUniversity of EssexIn this study, a generic analysis of sensor impulse response effects on linearly filtered channel noise is presented to illustrate the resulting variations to the Cramèr–Rao lower bounds (CRLBs) of signal parameter estimators in signal processing and communication applications. The authors start by deriving the density function of a filtered signal, which is shown to be a mixture density, and hence the exact expressions for the mean and variance. Simulation results are used to confirm the derivations, which are then used to investigate the effects of impulse response length and variance, as well as channel noise length and variance effects on the resulting CRLBs. Results indicate that for non-zero-mean channel noise and impulse responses, the resulting mean of filtered noise can be relatively large causing adverse deviations to parameter estimations. The filtered noise variance is shown to be proportional to the impulse response energy, where for long duration of signal capture the CRLB is significantly increased.http://digital-library.theiet.org/content/journals/10.1049/joe.2014.0230signal processingtransient responseparameter estimationsensor impulse response effectsCramer–Rao lower boundssignal parameter estimatorslinearly filtered channel noisesignal processingdensity functionmixture densitynonzero-mean channel noiseparameter estimationfiltered noise variance
collection DOAJ
language English
format Article
sources DOAJ
author Ephraim S. Gower
Malcom O.J. Hawksford
spellingShingle Ephraim S. Gower
Malcom O.J. Hawksford
Analysis of sensor impulse response effects on Cramèr–Rao lower bounds for signal parameter estimators
The Journal of Engineering
signal processing
transient response
parameter estimation
sensor impulse response effects
Cramer–Rao lower bounds
signal parameter estimators
linearly filtered channel noise
signal processing
density function
mixture density
nonzero-mean channel noise
parameter estimation
filtered noise variance
author_facet Ephraim S. Gower
Malcom O.J. Hawksford
author_sort Ephraim S. Gower
title Analysis of sensor impulse response effects on Cramèr–Rao lower bounds for signal parameter estimators
title_short Analysis of sensor impulse response effects on Cramèr–Rao lower bounds for signal parameter estimators
title_full Analysis of sensor impulse response effects on Cramèr–Rao lower bounds for signal parameter estimators
title_fullStr Analysis of sensor impulse response effects on Cramèr–Rao lower bounds for signal parameter estimators
title_full_unstemmed Analysis of sensor impulse response effects on Cramèr–Rao lower bounds for signal parameter estimators
title_sort analysis of sensor impulse response effects on cramèr–rao lower bounds for signal parameter estimators
publisher Wiley
series The Journal of Engineering
issn 2051-3305
publishDate 2014-12-01
description In this study, a generic analysis of sensor impulse response effects on linearly filtered channel noise is presented to illustrate the resulting variations to the Cramèr–Rao lower bounds (CRLBs) of signal parameter estimators in signal processing and communication applications. The authors start by deriving the density function of a filtered signal, which is shown to be a mixture density, and hence the exact expressions for the mean and variance. Simulation results are used to confirm the derivations, which are then used to investigate the effects of impulse response length and variance, as well as channel noise length and variance effects on the resulting CRLBs. Results indicate that for non-zero-mean channel noise and impulse responses, the resulting mean of filtered noise can be relatively large causing adverse deviations to parameter estimations. The filtered noise variance is shown to be proportional to the impulse response energy, where for long duration of signal capture the CRLB is significantly increased.
topic signal processing
transient response
parameter estimation
sensor impulse response effects
Cramer–Rao lower bounds
signal parameter estimators
linearly filtered channel noise
signal processing
density function
mixture density
nonzero-mean channel noise
parameter estimation
filtered noise variance
url http://digital-library.theiet.org/content/journals/10.1049/joe.2014.0230
work_keys_str_mv AT ephraimsgower analysisofsensorimpulseresponseeffectsoncramerraolowerboundsforsignalparameterestimators
AT malcomojhawksford analysisofsensorimpulseresponseeffectsoncramerraolowerboundsforsignalparameterestimators
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