When it comes to sensing the environment (RADAR, imaging, seismic, ...), the current trend is to develop acquisition systems that are more and more sophisticated. For example, we can point out an increase in the number of sensors, the use of multiple arrays for either emission or reception, as well as the integration of several modalities like polarization, interferometry, temporal, spatial and spectral information, or waveforms diversity. Obviously, this sophistication is made to enrich the obtained information and to reach better performances compared to classical systems, such as improving the resolution, improving detection performance (especially for low SNR settings), or allowing a better discrimination between physical phenomena. However, the simple transposition of classical process/algorithms in these new systems does not necessary led to the expected improved performances. Indeed, several effects impose to deeply re-derive the modelizations and the processes: - the answer of the sensed environment becomes complex and heterogeneous, - the size of the data is increased, so the estimation of statistical parameters may become difficult, - in systems with multiple modalities, the construction of the data vector is nontrivial, - there are more uncertainties on the model of the useful signal (therefore on its parameterization) The MARGARITA project aims at solving the aforementioned issues by developing new estimation/detection processes for multi-sensors/multi-modal systems operating in a complex heterogeneous environment. These new methods will be based upon the combination of recent tools and advances in signal processing: robust estimation, optimization methods, differential geometry and large random matrices theory. Hence, the project aims at: + integrating an accurate statistical modeling (i.e. handling non Gaussianity and heterogeneity) for estimation/detection problems in large dimension settings. + integrating prior information and model uncertainties in a modern robust estimation/detection framework. + accurately characterizing the theoretical performances of the developed processes. Apart from providing theoretical guarantees, this characterization will also offer tools for system design and specification. + Demonstrating that the proposed tools can be applied in fields that involve modern acquisition systems. We propose to adapt these processes to specific radar applications (STAP, MIMO-STAP, SAR) as well as other civilian applications (Hyperspectral imaging, radio-astronomy and GPR) From a scientifical and technical perspective, this project will: - use tools from the robust estimation framework and the optimization framework (majorization-minimization and optimization on manifolds) to propose new estimators (notably for covariance matrices) that exploit available prior information to counter the large dimension problem. - extend the Bayesian subspace estimation methods to a robust estimation/detection framework in order to integrate uncertainties on the signal model. - exploit the misspecified performance bounds framework to solve the problem of multi-sensors/multi-modal systems calibration. - use recent theoretical tools (large random matrices theory and intrinsic bounds) to characterize the performances of the developed processes.