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...
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| Published in: | Digital Signal Processing with Kernel Methods pp. 97 - 164 |
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| Main Authors: | , , , |
| Format: | Book Chapter |
| Language: | English |
| Published: |
Chichester, UK
Wiley
2018
John Wiley & Sons, Ltd |
| Edition: | 1 |
| Series: | Wiley - IEEE |
| Subjects: | |
| ISBN: | 9781118611791, 1118611799 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Muñoz-Mar&iacute, Jordi Rojo-Álvarez, José Luis Camps-Valls, Gustau Martínez-Ramón, Manel |
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| Copyright | 2018 Wiley 2018 John Wiley & Sons Ltd. |
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| Keywords | Correlation Signal processing algorithms Estimation Signal processing Hilbert space Data models Kernel |
| Language | English |
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| PublicationTitle | Digital Signal Processing with Kernel Methods |
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| Snippet | This chapter conveys the ideas from digital signal processing (DSP) to be clearly kept in mind when working on kernel‐based signal processing... 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... |
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| StartPage | 97 |
| SubjectTerms | autoregressive exogenous signal model autoregressive moving average model digital signal processing Hilbert spaces kernel functions kernel‐based signal processing algorithms vector spaces |
| Title | Signal Processing Models |
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