Soft‐decision schemes for radar estimation of elevation at low grazing angles

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.