In this paper we devise and assess two procedures for selecting secondary data in a very heterogeneous radar scenario including the presence of outliers. The new algorithms are based on the theory of the Generalized Likelihood Function, either one-step or two-step, and achieve an effective rejection of the outliers. We also present performance curves showing that, for some operational scenarios with more than one outliers, the new procedures can outperform some previously proposed data selection algorithms. © 2004 IEEE.
Data-adaptive training selection for radar applications / Conte, Ernesto; DE MAIO, Antonio; A., Farina; G., Foglia. - ELETTRONICO. - (2004), pp. 179-182. (Intervento presentato al convegno International Symposium on Signal Processing & Information technology 2004 tenutosi a Rome, Italy nel 2004) [10.1109/ISSPIT.2004.1433716].
Data-adaptive training selection for radar applications
CONTE, ERNESTO;DE MAIO, ANTONIO;
2004
Abstract
In this paper we devise and assess two procedures for selecting secondary data in a very heterogeneous radar scenario including the presence of outliers. The new algorithms are based on the theory of the Generalized Likelihood Function, either one-step or two-step, and achieve an effective rejection of the outliers. We also present performance curves showing that, for some operational scenarios with more than one outliers, the new procedures can outperform some previously proposed data selection algorithms. © 2004 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
