The main subject of this thesis is the study of algorithms for non-cooperative targets recognition (NCTR). The purpose is to make recognition within "fighter" class using range profile. The study of four algorithms is proposed : one based on the KNN algorithm, one on probabilistic methods and two on fuzzy logic. A major constraint of NCTR algorithms is to control the error rate while maximizing the success rate. We have shown that the two first algorithms are not sufficient to fulfill this requirement. On the other hand, two algorithms based on fuzzy logic have been proposed and meet this requirement. Compliance with this condition is made at the expense of success rate (in particular on real data) for the first of the two algorithms based on fuzzy-logic. However, a second version of the algorithm has greatly increased the success rate while keeping control of the error rate. The principle of this algorithm is to make classification range bin by range bin, with the introduction of data acquired in an anechoic chamber. We also proposed a procedure for adapting the data acquired in an anechoic chamber for a class to another class of targets. The second major constraint algorithms NCTR is the real time constraint. An advanced study of a parallelization on GPU of the algorithm based on KNN was conducted at the beginning of the thesis. This study has helped to identify key points of a parallelization on GPU of NCTR algorithms. Findings from this study will be used to parallelize efficiently on GPU future NCTR algorithms, including those proposed in the thesis.
