The problem of missing sensor measurements can emerge in a variety of radar signal processing applications as for instance beamforming, direction of arrival estimation, interference cancellation, and target detection. The mentioned applications rely on reliable data covariance matrix estimates and suitable procedures, based on the expectation-maximization (EM) algorithm, have been proposed in the open literature to cope with the lack of some entries within specific spatial snapshots. In this paper, the effectiveness of a recent structured covariance matrix estimator [1], accounting for missing data and leveraging possible structural knowledge, is assessed on measured data. Specifically, the estimation procedure is framed in the context of two practically relevant radar applications: beamforming and detection of the number of sources. At the analysis stage, results highlight the effectiveness of the procedure to tackle missing data in the considered radar scenarios.

Experimental analysis of structured covariance estimators with missing data / Rosamilia, M.; Aubry, A.; Carotenuto, V.; De Maio, A.. - (2021), pp. 271-276. (Intervento presentato al convegno 8th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2021 nel 2021) [10.1109/MetroAeroSpace51421.2021.9511731].

Experimental analysis of structured covariance estimators with missing data

Rosamilia M.;Aubry A.;Carotenuto V.;De Maio A.
2021

Abstract

The problem of missing sensor measurements can emerge in a variety of radar signal processing applications as for instance beamforming, direction of arrival estimation, interference cancellation, and target detection. The mentioned applications rely on reliable data covariance matrix estimates and suitable procedures, based on the expectation-maximization (EM) algorithm, have been proposed in the open literature to cope with the lack of some entries within specific spatial snapshots. In this paper, the effectiveness of a recent structured covariance matrix estimator [1], accounting for missing data and leveraging possible structural knowledge, is assessed on measured data. Specifically, the estimation procedure is framed in the context of two practically relevant radar applications: beamforming and detection of the number of sources. At the analysis stage, results highlight the effectiveness of the procedure to tackle missing data in the considered radar scenarios.
2021
978-1-7281-7556-0
Experimental analysis of structured covariance estimators with missing data / Rosamilia, M.; Aubry, A.; Carotenuto, V.; De Maio, A.. - (2021), pp. 271-276. (Intervento presentato al convegno 8th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2021 nel 2021) [10.1109/MetroAeroSpace51421.2021.9511731].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/923290
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