Resumen

GALVEZ, N.B.; COUSSEAU, J.E.; PASCIARONI, J.L.  y  AGAMENNONI, O.E.. Improved neural network based CFAR detection for non homogeneous background and multiple target situations. Lat. Am. appl. res. [online]. 2012, vol.42, n.4, pp. 343-350. ISSN 0327-0793.

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 effectiveness of neural networks to search for non homogeneities like clutter banks and multiple targets within the radar return. In addition, the methodology 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 verified that the new proposal presents a robust operation when maintaining a constant probability of false alarm under different radar return situations.

Palabras llave : Neural Networks; Threshold; CFAR; Clutter; Multiple Targets; Detection.