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.

Environment monitoring is crucial for understanding the relationship between climate change and changes in large scale earth structures such as glaciers and forests. For these big structures, monitoring temporal evolution or assessing resilience and adaptation of earth to changes requires the analysis of time series composed of images. When considering remote sensing imagery, analyzing such time series is actually facing dimensionality: observations are huge data both in time and space domains; in addition with intricacy when using coherent acquisition waves (radar imaging for instance). The challenge of remote sensing information science is then developing tools for handling dimensionality of data. The scientific objective of the PHOENIX project is to provide non-stationary multidimensional models for easing information mining and retrieval in long sequences of multisource/distributed image time series issued from recent constellations of satellites. These models will be used to characterize the evolution of earth structures such as glaciers and forests. The technical objective of the PHOENIX project is to provide resilience analysis from information modeling and retrieval in image time series of Alpine glaciers and Amazonian forests. This analysis will be performed through a general framework of random field time series, with two work packages (WP) dedicated to methodological developments. The first package, WP1, will address parsimonious parametric modeling of random field time series by using non-stationary fractionally differenced/integrated parameterizations. The second package, WP2, is dedicated to non-parametric methods for the analysis of random field time series: cumulant analysis and trend/stationary decompositions are some important topics addressed in this WP. The third package, WP3, will focus on the application of WP1 and WP2 methods to 2 kinds of mono/multi-channel earth observation satellite image time series: 1) Synthetic Aperture Radar images which cover large areas and are not impacted by meteorological variability, 2) Spectro-Visible images which can observe specific areas with a higher spatio-spectral resolution. WP3 requires High Performance Computing (HPC). HPC will deserve two types of architectures: a big cluster of CPU (USMB MUST, already operational, efficient for parallel computing on “large databases with small size data”, but limited for loading and processing huge data) and a specific scalable workstation with huge random access memory (ANR support requested) for loading and processing huge size image time series.