Consiglio Nazionale delle Ricerche

Tipo di prodottoRapporto tecnico
TitoloAnalysis of Inhomogeneous Random Sampling
Anno di pubblicazione2015
FormatoElettronico
Autore/iFlavio Zabini, Andrea Conti
Affiliazioni autoriWiLAB, University of Bologna ENDIF and WiLAB, University of Ferrara
Autori CNR e affiliazioni
  • ANDREA CONTI
Lingua/e
  • inglese
SintesiProcess estimation from randomly deployed samples in a multidimensional space with sample position errors is essential for various applications. This analyzes random sampling in R^d jointly accounting for finite-energy process properties (process spectrum and spatial correlation) and for sampling properties (inhomogeneous sample spatial distribution, sample availability, and non-ideal knowledge of sample positions). Based on process and sampling properties, the estimated process spectrum and the estimation accuracy are derived. Some properties expand the process spectrum while others modify the process without expansion. The process estimation accuracy is determined in a general case. The analysis is corroborated by verifying that previously known results can be obtained as special cases of the general one and by means of a case study accounting for various process and sample properties.
Lingua sintesieng
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Pagine da1
Pagine a22
Pagine totali22
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Numero volume della serie/collana-
ISBN-
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Parole chiaverandom sampling, process estimation, inhomogeneous Poisson
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Strutture CNR
  • IEIIT — IEIIT - Sede secondaria di Bologna
Moduli CNR
    Progetti Europei-
    Allegati
    • Analysis of Inhomogeneous Random Sampling
      Descrizione: Process estimation from randomly deployed samples in a multidimensional space with sample position errors is essential for various applications. This analyzes random sampling in R^d jointly accounting for finite-energy process properties (process spectrum and spatial correlation) and for sampling properties (inhomogeneous sample spatial distribution, sample availability, and non-ideal knowledge of sample positions). Based on process and sampling properties, the estimated process spectrum and the estimation accuracy are derived. Some properties expand the process spectrum while others modify the process without expansion. The process estimation accuracy is determined in a general case. The analysis is corroborated by verifying that previously known results can be obtained as special cases of the general one and by means of a case study accounting for various process and sample properties.