Performance comparison of a linear parametric noise estimation Wiener filter and non-linear joint transform correlator for realistic clutter backgrounds
| Autor: | David M. Budgett, John D. Richardson, Chris Chatwin, Sovira Tan, Rupert Young |
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| Rok vydání: | 2000 |
| Předmět: |
Computer science
business.industry Wiener filter Wiener deconvolution Spectral density Filter (signal processing) Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials symbols.namesake Filter design Optics Computer Science::Computer Vision and Pattern Recognition Optical correlator Parametric model symbols Clutter Electrical and Electronic Engineering Physical and Theoretical Chemistry business Root-raised-cosine filter |
| Zdroj: | Optics Communications. 182:83-90 |
| ISSN: | 0030-4018 |
| Popis: | It has been shown previously that a linear Wiener filter is capable of detecting a target in severe clutter backgrounds by utilising a parametric model of the clutter power spectrum in its filter transfer function. In this paper the performance of the linear Wiener filter is compared to that implemented in a non-linear joint transform correlator in which the entire current input scene is used as an approximation for the clutter background. Realistic clutter backgrounds are employed in the tests that cover a range of natural scenery likely to be encountered in practice. The linear Wiener filter, employing a parametric model of the averaged background scenes, is shown to outperform the non-linear filter in most cases. Brief consideration is also given to the relative merits of implementation of these two filters in both optical and digital correlators. |
| Databáze: | OpenAIRE |
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