INVESTIGADORES
COUSSEAU Juan Edmundo
artículos
Título:
Improved neural network based CFAR detection for non homogeneous background and multiple target situations
Autor/es:
N.B. GÁLVEZ; J. E. COUSSEAU; J.L. PASCIARONI; O.E. AGAMENNONI
Revista:
LATIN AMERICAN APPLIED RESEARCH
Editorial:
PLAPIQUI(UNS-CONICET)
Referencias:
Año: 2012
ISSN:
0327-0793
Resumen:
The Neural Network Cell Average - Order Statistics Constant False Alarm Rate (NNCAOS CFAR) detector is presented in this work. NNCAOS CFAR is a combined detection methodology which uses the e fectiveness of neural networks to search for non ho-mogeneities like clutter banks and multiple targets within the radar return. In addition, themethodology proposed applies a convenient cell average (CA) or order statistics (OS) CFAR detector according to the context situation. Exhaustive analysis and comparisons show that NNCAOS CFAR has better performance than CA CFAR, OS CFAR and even CANN CFAR detectors (the latter, a previously proposed neural network based detector). Furthermore, it is veried that the new proposal presents a robust operation when maintaining a constant probability of false alarm under di erent radar return situations.
