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Abstract
In modern radars, the problem of estimating elevation angle at low grazing angles is typically solved using superresolution techniques. These techniques often require one to provide an estimate of the number of waveforms impinging the array, which one can accomplish using model selection techniques. In this paper, we investigate the performance of an alternative approach, based on the Bayesian-like model averaging. The Bayesian approach exploits the fact that the parameters of the model related to multipath signals are nuisance ones, which allows one to avoid the estimation of the number of waveforms and improves estimation performance. The method is introduced for the classical conditional maximum likelihood estimator and extended to its, recently proposed, robustified version. We find, however, that the robustified estimator includes its own soft-decision mechanism and benefits from the averaging only for low levels of model uncertainty.
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- Copyright (2019 The Institution of Engineering and Technology)
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- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
IET Radar Sonar and Navigation
no. 13,
pages 1934 - 1942,
ISSN: 1751-8784 - Language:
- English
- Publication year:
- 2019
- Bibliographic description:
- Meller M., Stawiarski K.: Soft-decision schemes for radar estimation of elevation at low grazing angles// IET Radar Sonar and Navigation. -Vol. 13, iss. 11 (2019), s.1934-1942
- DOI:
- Digital Object Identifier (open in new tab) 10.1049/iet-rsn.2019.0042
- Verified by:
- Gdańsk University of Technology
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