A new class of disturbance covariance matrix estimators for radar signal processing applications is introduced following a geometric paradigm. Each estimator is associated with a given unitary invariant norm and performs the sample covariance matrix projection into a specific set of structured covariance matrices. Regardless of the considered norm, it is shown that the new class of distribution-free estimators shares a shrinkage-type form; besides, the eigenvalues estimate just requires the solution of a one-dimensional convex problem whose objective function depends on the considered unitary norm. At the analysis stage, the effectiveness of the new estimators is assessed in terms of achievable Signal to Interference plus Noise Ratio (SINR) also in comparison with some existing counterparts. © 2017 IEEE.
Aubry, A., De Maio, A., Pallotta, L. (2017). A geometric approach for structured radar covariance estimation. In 2017 IEEE Radar Conference, RadarConf 2017 (pp.0767-0771). Institute of Electrical and Electronics Engineers Inc. [10.1109/RADAR.2017.7944306].