Signal Processing Models

This chapter conveys the ideas from digital signal processing (DSP) to be clearly kept in mind when working on kernel‐based signal processing algorithms. It presents the basic concepts of signal Hilbert spaces, noise, and optimization. The chapter provides a brief overview of vector space...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Digital Signal Processing with Kernel Methods s. 97 - 164
Hlavní autoři: Rojo-Álvarez, José Luis, Martínez-Ramón, Manel, Muñoz-Mar&iacute, Jordi, Camps-Valls, Gustau
Médium: Kapitola
Jazyk:angličtina
Vydáno: Chichester, UK Wiley 2018
John Wiley & Sons, Ltd
Vydání:1
Edice:Wiley - IEEE
Témata:
ISBN:9781118611791, 1118611799
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This chapter conveys the ideas from digital signal processing (DSP) to be clearly kept in mind when working on kernel‐based signal processing algorithms. It presents the basic concepts of signal Hilbert spaces, noise, and optimization. The chapter provides a brief overview of vector spaces and basis. It discusses the general signal model and introduces specific signal models for the most common problems in DSP; namely, nonparametric spectral estimation, system identification, interpolation, deconvolution, and array processing. The chapter then introduces two signal models for system identification that will be used in the following chapters when dealing with kernel functions; namely, the autoregressive exogenous (ARX) signal model and a particular instantiation of an autoregressive moving average model (ARMA) filter called the γ‐filter. It further presents the state‐space models and signal models which are used for recursion in many DSP applications. Finally, the chapter discusses the main aspects of the signal models encountered in DSP.
ISBN:9781118611791
1118611799
DOI:10.1002/9781118705810.ch3