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Adaptive Filters and Observers

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Digital Signal Processing with Matlab Examples, Volume 2

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Abstract

In signal processing, as well as in other fields, it is always advisable to take advantage of all the ‘a priori’ knowledge available about the problem in hand. Part of this knowledge could be expressed with mathematical models. Just after the chapter introduction, the next section introduces the Wiener filter, which is based on linear estimation. The filter uses models of the noise and the signal, and is quite successful for denoising. Section three shows that the filter coefficients can be recursively estimated during the process of filtering. This paves the way for formulating adaptive filters, with various applications. Section five contains an interesting example of application, which is image deblurring. The chapter continues with Bayesian estimation, with especial reference to image restoration. This section includes the Lucy-Richardson algorithm. After this, in view of aspects of interest for next chapters, the chapter presents a section on observers. Finally, there is a section with some interesting experiments.

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Authors and Affiliations

  1. Systems Engineering and Automatic Control, Universidad Complutense de Madrid, Madrid, Spain

    Jose Maria Giron-Sierra

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  1. Jose Maria Giron-Sierra
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Correspondence to Jose Maria Giron-Sierra .

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Giron-Sierra, J.M. (2017). Adaptive Filters and Observers. In: Digital Signal Processing with Matlab Examples, Volume 2. Signals and Communication Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-2537-2_5

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  • DOI: https://doi.org/10.1007/978-981-10-2537-2_5

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