LITpro: a model fitting software for optical interferometry

9 pagesInternational audienceLITpro is a software for fitting models on data obtained from various stellar optical interferometers, like the VLTI. As a baseline, for modeling the object, it provides a set of elementary geometrical and center-to-limb darkening functions, all combinable together. But it is also designed to make very easy the implementation of more specific models with their own parameters, to be able to use models closer to astrophysical considerations. So LITpro only requires the modeling functions to compute the Fourier transform of the object at given spatial frequencies, and wavelengths and time if needed. From this, LITpro computes all the necessary quantities as needed (e.g. visibilities, spectral energy distribution, partial derivatives of the model, map of the object model). The fitting engine, especially designed for this kind of optimization, is based on a modified Levenberg-Marquardt algorithm and has been successfully tested on real data in a prototype version. It includes a Trust Region Method, minimizing a heterogeneous non-linear and non-convex criterion and allows the user to set boundaries on free parameters. From a robust local minimization algorithm and a starting points strategy, a global optimization solution is effectively achieved. Tools have been developped to help users to find the global minimum. LITpro is also designed for performing fitting on heterogeneous data. It will be shown, on an example, how it fits simultaneously interferometric data and spectral energy distribution, with some benefits on the reliability of the solution and a better estimation of errors and correlations on the parameters. That is indeed necessary since present interferometric data are generally multi-wavelengths

Towards a probabilistic regional reanalysis for Europe

A new development in the field of reanalyses is the incorporation of uncertainty estimation capabilities. In the course of this work, a new probabilistic regional reanalysis system for the CORDEX-EUR11 domain has been developed. It is based on the numerical weather prediction model COSMO at a 12 km grid spacing. The lateral boundary conditions of all ensemble members are provided by the global reanalysis ERA-Interim. In the basic implementation of the system, uncertainties due to observation errors are estimated. Atmospheric assimilation of conventional observations perturbed by means of random samples of observation error yields estimates of the reanalysis uncertainty conditioned to observation errors. The data assimilation employed is a new scheme based on observation nudging that is denoted ensemble nudging. The lower boundary of the atmosphere is regularly updated by external snow depth, sea surface temperature and soil moisture analyses. One of the most important purposes of reanalyses is the estimation of so-called essential climate variables. For regional reanalyses, precipitation has been identified as one of the essential climate variables that are potentially better represented than in other climate data sets. For that reason, the representation of precipitation in the system is assessed in a pilot study. Based on two experiments, each of which extends over one month, a preliminary comparison to the global reanalysis ERA-Interim, a dynamical downscaling of the latter and the high-resolution regional reanalysis COSMO-REA6 is conducted. In a next step, the probabilistic capabilities of the reanalysis system are assessed versus the ECMWF-EPS in terms of six-hourly precipitation sums. The added value of the probabilistic regional reanalysis system motivates the current production of a 5-year long test reanalysis COSMO-EN-REA12 in the framework of the FP7-funded project Uncertainties in Ensembles of Regional Reanalyses (UERRA). To provide an indication of how the probabilistic reanalysis system would be configured ideally for future long-term reanalyses, it is updated to a new model version of COSMO and extended by further uncertainty estimation capabilities. Now, model error can be accounted for by the method of stochastic perturbation of physical tendencies. Further, uncertainties in the lateral boundary conditions are incorporated by utilizing a global ensemble of the new numerical weather prediction model ICON. A comparative verification of screen-level temperature as second important essential climate variable and precipitation in a range of numerical experiments with different configurations of the reanalysis system indicates that the best probabilistic capabilities are achieved by accounting for as many uncertain components as possible

Comparison of Current Gravity Estimation and Determination Models

This paper will discuss the history of gravity estimation and determination models while analyzing methods that are in development. Some fundamental methods for calculating the gravity field include spherical harmonics solutions, local weighted interpolation, and global point mascon modeling (PMC). Recently, high accuracy measurements have become more accessible, and the requirements for high order geopotential modeling have become more stringent. Interest in irregular bodies, accurate models of the hydrological system, and on-board processing has demanded a comprehensive model that can quickly and accurately compute the geopotential with low memory costs. This trade study of current geopotential modeling techniques will reveal that each modeling technique has a unique use case. It is notable that the spherical harmonics model is relatively accurate but poses a cumbersome inversion problem. PMC and interpolation models, on the other hand, are computationally efficient, but require more research to become robust models with high levels of accuracy. Considerations of the trade study will suggest further research for the point mascon model. The PMC model should be improved through mascon refinement, direct solutions that stem from geodetic measurements, and further validation of the gravity gradient. Finally, the potential for each model to be implemented with parallel computation will be shown to lead to large improvements in computing time while reducing the memory cost for each technique.Aerospace Engineering and Engineering Mechanic

Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications (e.g., highly accurate 3D environment reconstruction and mapping, high precision object recognition and localization, ...). In this paper, we propose a human-friendly, reliable and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple. Our approach is based on a novel two components error model. This model unifies the error sources of RGB-D pairs based on different technologies, such as structured-light 3D cameras and time-of-flight cameras. Our method provides some important advantages compared to other state-of-the-art systems: it is general (i.e., well suited for different types of sensors), based on an easy and stable calibration protocol, provides a greater calibration accuracy, and has been implemented within the ROS robotics framework. We report detailed experimental validations and performance comparisons to support our statements

Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging

The implementation challenges of cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging are discussed and work on the subject is reviewed. System architecture and sensor fusion are identified as key challenges. A partially decentralized system architecture based on step-wise inertial navigation and step-wise dead reckoning is presented. This architecture is argued to reduce the computational cost and required communication bandwidth by around two orders of magnitude while only giving negligible information loss in comparison with a naive centralized implementation. This makes a joint global state estimation feasible for up to a platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion for the considered setup, based on state space transformation and marginalization, is presented. The transformation and marginalization are used to give the necessary flexibility for presented sampling based updates for the inter-agent ranging and ranging free fusion of the two feet of an individual agent. Finally, characteristics of the suggested implementation are demonstrated with simulations and a real-time system implementation.Comment: 14 page