Cette thèse se focalise sur le concept du radar MIMO co-localisé. L'acronyme MIMO -- pour Multiple-Input Multiple-Output -- indique l'utilisation de plusieurs émetteurs et de plusieurs récepteurs, tandis que le terme co-localisé signifie que ces éléments sont étroitement espacés. Chaque émetteur envoie une forme d'onde qui lui est propre : un radar MIMO émet donc simultanément un ensemble de signaux.Cette thèse a ainsi pour but d'établir une méthodologie permettant de générer cet ensemble de signaux, tout en respectant certaines contraintes opérationnelles. Cela nous permettra de déterminer les apports éventuels de ce radar. Nous nous sommes intéressés en particulier aux codes de phase, pour des raisons de couplage (qu'on peut traduire ici par la capacité, lors du traitement, à distinguer la position angulaire d'une cible de sa distance).La méthodologie proposée se synthétise simplement en une modélisation sous la forme d'un problème d'optimisation. Contrairement à la littérature et à des précédents résultats théoriques, nous avons décidé d'évaluer l'orthogonalité des signaux émis par le radar en différentes directions, et non l'orthogonalité des signaux élémentaires. Ce problème, plus réaliste, est malheureusement non-convexe et à grande échelle : un benchmark sur différentes méthodes d'optimisation nous a permis de constater l'efficacité des algorithmes basées sur le gradient.Optimiser cette orthogonalité sous-entend l'utilisation de filtres adaptés. Cependant, en pratique, le traitement radar s'effectue à l'aide de filtres désadaptés. Nous suggérons ainsi un problème d'optimisation jointe, permettant de générer de manière simultanée un ensemble de formes d'onde (pour le radar MIMO, entre autres) et les filtres désadaptés associés. Des simulations ont permis de montrer l'efficacité de la méthode. Celle-ci est en particulier préférable aux algorithmes cycliques habituellement utilisés. / This thesis deals with coherent MIMO radars. MIMO stands for Multiple-Input Multiple-Output, meaning that several transmitters and several receivers are used, closely-spaced in a coherent MIMO radar. Each transmitter has its own signal, providing waveform diversity. This thesis aims for defining a way to generate a set of sequences, specific for this radar, while satisfying practical constraints. It may help to determine the potential contribution of a MIMO radar. Only phase codes are concerned here, because they suffer less from the range/angle coupling effect.A simple framework is introduced, based on an optimisation problem.While literature often involves the orthogonality of the elementary signals (because of theoretical aspects), it is suggested to consider the orthogonality of signals from different directions of the surveillance space. Unfortunately, the obtained optimisation problem is non-convex and has a lot of variables. A benchmark on a simpler problem notifies us that gradient-based algorithms are surprisingly efficient.An optimisation of the correlation function corresponds to a processing with matched filters. However, in practice, mismatched filters are usually employed. A joint optimisation problem is suggested accordingly, in order to generate simultaneously a set of sequences (e.g. MIMO radar signals) and their associated mismatched filters. Obtained results are quite promising : as expected, a joint optimisation seems to perform better than a cyclic one, usually employed.

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<p>This thesis concerns with the localization, tracking, and sensor management in the Multiple-Input Multiple-Output (MIMO) radar systems. The collocated and widely-separated MIMO radars are separately discussed and the signal models are derived for both structures.</p> <p>The first chapter of the thesis is dedicated to the tracking and localization in collocated MIMO radars. A novel signal model is first formulated and the localization algorithm is developed for the derived signal model to estimate the location of multiple targets falling in the same resolution cell. Furthermore, a novel tracking algorithm is proposed in which the maximum bound on the number of uniquely detectable targets in the same cell is relaxed. The performance of the tracking and localization algorithms is finally evaluated using the tracking Posterior Cramer-Rao Lower Bound (PCRLB).</p> <p>After showing the impact of the antennas position on the localization CRLB, a novel sensor management technique is developed for the collocated MIMO radars in Chapter 4. A convex optimization technique is proposed for the antenna allocation in a single-target scenario. When multiple targets fall inside the same cell, a sampling-based technique is formulated to tackle the non-convexity of the optimization problem.</p> <p>The third chapter of this thesis also proposes new approaches for detection, localization, and tracking using a widely-separated MIMO radar. A scenario with multiple-scatterer targets is considered and the detection performance of both MIMO and multistatic radars will be evaluated in the designed scenario. To estimate the location of the multiple-scatterer target, a Multiple-Hypothesis (MH) based approach is proposed where the number and the location of multiple targets are both estimated. A particle filter based approach is also formulated for the dynamic tracking by a widely-separated MIMO radar. Finally, the performance of the MIMO radar and the miultistatic radar in detecting and localizing multiple-scatterer targets is studied.</p> / Doctor of Philosophy (PhD)