IIIE   20352
INSTITUTO DE INVESTIGACIONES EN INGENIERIA ELECTRICA "ALFREDO DESAGES"
Unidad Ejecutora - UE
artículos
Título:
Efficient non homogeneous CFAR processing
Autor/es:
NÉLIDA GÁLVEZ; J. E. COUSSEAU; J.L. PASCIARONI; O.E. AGAMENNONI
Revista:
LATIN AMERICAN APPLIED RESEARCH
Editorial:
PLAPIQUI(UNS-CONICET)
Referencias:
Año: 2010
ISSN:
0327-0793
Resumen:
In this work a new radar detection method is proposed, the Cell Average Neural Network Constant false Alarm Rate (CANN CFAR), which can be used with Weibull distributed non homogeneous radar returns. This processor combines Maximum Likelihood estimation method with Neural Networks for the clutter parameter estimation, resolving homogeneity and determining clutter bank transition points and size. To characterize its performance, probability of detectionis evaluated using Monte Carlo simulations and compared to other efficient CFAR schemes.As a result, CANN CFAR detection has better performance than conventional CFAR processors,especially when detecting targets located near clutter heterogeneities. An additionaladvantage of the proposed technique is its efficiency when determining clutter transitionpoints, bank size and threshold setting. This efficiency translates in lower computationtime than other CFAR algorithms, mostly considering real time processing.