SciELO - Scientific Electronic Library Online

vol.42 issue4Mathematical investigation over the Lewis number effect on the combustion of biomass particlesCo oxidation: effect of Ce and Au addition on MnOx catalysts author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand




  • Have no cited articlesCited by SciELO

Related links

  • Have no similar articlesSimilars in SciELO


Latin American applied research

Print version ISSN 0327-0793


GALVEZ, N.B.; COUSSEAU, J.E.; PASCIARONI, J.L.  and  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.

Keywords : Neural Networks; Threshold; CFAR; Clutter; Multiple Targets; Detection.

        · text in English     · English ( pdf )


Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License