NDLI: Soft-decision schemes for radar estimation of elevation at low grazing angles

Content Provider	IET Digital Library
Author	Meller, Michal Stawiarski, Kamil
Abstract	In modern radars, the problem of estimating elevation angle at low grazing angles is typically solved using super-resolution 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 study, the authors 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. The authors find, however, that the robustified estimator includes its own soft-decision mechanism and benefits from the averaging only for low levels of model uncertainty.
Starting Page	1934
Ending Page	1942
Page Count	9
ISSN	17518784
Volume Number	13
e-ISSN	17518792
Issue Number	Issue 11, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/13/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0042
Journal	IET Radar, Sonar & Navigation
Publisher Date	2019-06-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Bayesian Approach Classical Conditional Maximum-likelihood Estimator Direction of Arrival Estimation Elevation Angle Estimation Performance Low Grazing Angles Maximum Likelihood Estimation Model Selection Technique Model Uncertainty Modern Radars Radar Estimation Robustified Estimator Signal Processing And Detection Soft-decision Mechanism Soft-decision Scheme Statistics Super-resolution Technique
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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