The classification of static or mobile objects, from a signal or an image containing information as to their structure or their form, constitutes a constant concern of specialists in the electronic field. The remarkable progress made in past years, particularly in the development of neural networks and artificial intelligence systems, has further accentuated this trend. The fields of application and potential uses of Artificial Intelligence are increasingly diverse: understanding of natural language, visual recognition, robotics, autonomous system, Machine learning, etc. This paper is a state of the art on the classification of radar signals. It focuses on the contribution of artificial intelligence to the latter without forgetting target tracking. This by evoking the different feature extractors, classifiers and the existing identification deep learning algorithms. We detail also the process allowing carrying out this classification.
<span>The pulse ultra-wide band (UWB) radar consists of switching of energy of very short duration in an ultra-broadband emission chain, and the UWB signal emitted is an ultrashort pulse, of the order of nanoseconds, without a carrier. These systems can indicate the presence and distances of a distant object, call a target, and determine its size, shape, speed, and trajectory. In this paper, we present a UWB radar system allowing the detection of the presence of a target and its localization in a road environment based on the principle of correlation of the reflected signal with the reference and the determination of its correlation peak.</span>
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