The radio-frequency spectrum is a precious resource, with many applications and users, especially with the recent spectrum auction in the United States. Future platforms and devices, such as radars and radios, need to be adaptive to their spectral environment in order to continue serving the needs of their users. This thesis considers an environment with one tracking radar, a single target, and a communications system. The radar-communications coexistence problem is modeled as a Markov decision process (MDP), and reinforcement learning is applied to drive the radar to optimal behavior. / Master of Science / The radio-frequency electromagnetic spectrum is a precious resource, in which users and operators are assigned frequency slots in which they can operate. The federal spectrum auction in the United States freed up some of the spectrum for shared use. The implications of this are the spectrum will become more dense; there will be more devices and users in the same amount of spectrum. The devices and platforms of this spectrum need to be more adaptive and agile in order to (1) not be interfered by other systems, (2) cause interference to other systems, and (3) continue to meet the needs of users (e.g. cell phone users) and operators (e.g. military radar). The work presented in this thesis applies Markov decision process and reinforcement learning to solve the problem.

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Dans ce travail, une nouvelle approche de conduite des observations radar de la forêt est proposée. Elle combine des méthodes statistiques pour l’analyse de sensibilité et les plans d’expériences numériques séquentiels et un code de calcul simulant la rétrodiffusion d’une forêt en vue de l’élaboration d’un modèle approché (métamodèle) à moindre coût numérique. L’introduction de ces outils mathématiques a pour objectif d’aider à la planification et à l’exécution des simulations radar et à l’organisation et l’analyse de leurs résultats. D’une part, les techniques de l’analyse de sensibilité sont appliquées afin de classer par ordre d’importance les paramètres d’entrée du modèle et d’identifier les paramètres de la forêt les plus significatifs ainsi que leurs effets sur le signal radar. D’autre part, la construction d’un métamodèle adaptable accélère le code de calcul, en préservant la physique du phénomène. Le cadre opérationnel de ce modèle approché sert finalement à introduire le principe du radar cognitif dans notre stratégie. Dans ce cas, une analyse rapide du signal reçu est nécessaire pour concevoir, en temps réel, le nouveau signal à émettre. De cette façon, les observations du radar simulées incluent en temps réel l’effet de l’environnement illuminé grâce aux simulations plus rapides et ciblées. / In this work, a new approach to conduct the radar observations of forests is proposed. It combines statistical methods for sensitivity analysis and adaptive design of simulation experiments and a numerical code simulating the the forest backscattering for the use of a approximate model (metamodel) with less computational cost. The introduction of these mathematical tools has as an objective to assist the design and the execution of radar simulations and at the organization and the analysis of their results. On the one hand, the sensitivity analysis techniques were applied in order to classify the input parameters by means of their importance and to identify the most significant forest parameters as well as their effects on the radar signal. On the other hand, the construction of an adaptive metamodel accelerates the simulation model, while keeping the physics of the phenomenom. The operational frame of this approximate model serves finally in the introduction of the cognitive radar principle in our strategy. In that case, a fast analysis of the received signal is necessary to design, in real time, the new signal to be emitted. That way, the simulated radar observations take into account in real time the effect of the illuminated environment, thanks to the more focused and fast simulations.

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