In this article, we propose to apply 3-D printing technology to the design and realization of honeycomb microwave absorbers. First, printability of simple honeycomb structures, made of a lossy dielectric material, was evaluated and validated by measurement in the 2–18 GHz frequency band. Effective dielectric properties of honeycomb structures with different dimensions are also discussed. Then, the interest of 3-D printing technology was highlighted by the design, fabrication, and measurement of a multiscale honeycomb absorber that can significantly increase the relative bandwidth. The 3-D printed honeycomb absorbers are compared with others technologies. The potential of this technology was also illustrated by the fabrication and measurement of a pyramidal honeycomb absorber that demonstrates an absorption level of more than 22.9 dB over the 2–18 GHz frequency band.

Synthetic aperture radar (SAR) is one of the favorite tools for earth observation applications, i.e., oceanography, land use mapping, climate change since this device can offer the data at a high spatial resolution and in most meteorological conditions. This is more significant when the data acquired by the Sentinel-1, a new C-band satellite, are exploited. For high-resolution wind field extraction, two different approaches are proposed. In the scatterometry-based approach, wind direction is first extracted by the local gradient method at different scales, i.e., 1–5-km wind resolutions. It is then applied to the empirical geophysical model functions, i.e., CMOD (C-band), for surface wind speed estimation. The advantage of this approach is to deliver accurate wind speed estimates in the range of 2–25 m/s from different SAR data. However, it requires wind direction as an input parameter. This can lead to errors in wind speed estimation due to uncertain wind directions. Therefore, for comparison, in the second approach, we propose the use of the model without wind direction input proposed by Komarov et al. In general, the obtained wind fields based on two proposed approaches are quite similar, and they have good agreement with in situ measurements from the meteorological stations along the Iroise coast.

In the recent years, multi-constellation and multi-frequency have improved the positioning precision in GNSS applications and significantly expanded the range of applications to new areas and services. However, the use of multiple signals presents advantages as well as disadvantages, since they may contain poor quality signals that negatively impact the position precision. The objective of this study is to improve the Single Point Positioning (SPP) accuracy using multi-GNSS data fusion. We propose the use of robust-Extended Kalman Filter (referred to as robust-EKF hereafter) to eliminate outliers. The robust-EKF used in the present work combines the Extended Kalman Filter with the Iterative ReWeighted Least Squares (IRWLS) and the Receiver Autonomous Integrity Monitoring (RAIM). The weight matrix in IRWLS is defined by the MM Estimation method which is a robust statistics approach for more efficient statistical data analysis with high breaking point. The RAIM algorithm is used to check the accuracy of the protection zone of the user. We apply the robust-EKF method along with the robust combination of GPS, Galileo and GLONASS data from ABMF base station, which significantly improves the position accuracy by about 84% compared to the non-robust data combination. ABMF station is a GNSS reception station managed by Météo-France in Guadeloupe. Thereafter, ABMF will refer to the acronym used to designate this station. Although robust-EKF demonstrates improvement in the position accuracy, its outputs might contain errors that are difficult to estimate. Therefore, an algorithm that can predetermine the error produced by robust-EKF is needed. For this purpose, the long short-term memory (LSTM) method is proposed as an adapted Deep Learning-Based approach. In this paper, LSTM is considered as a de-noising filter and the new method is proposed as a hybrid combination of robust-EKF and LSTM which is denoted rEKF-LSTM. The position precision greatly improves by about 95% compared to the non-robust combination of data from ABMF base station. In order to assess the rEKF-LSTM method, data from other base stations are tested. The position precision is enhanced by about 87%, 77% and 93% using the rEKF-LSTM compared to the non-robust combination of data from three other base stations AJAC, GRAC and LMMF in France, respectively.

Wind information on SAR images are essential to characterize a marine environment in offshore or coastal area. More and more applications require high resolution wind field estimation. In this article, classical wind wave direction estimation methods are reviewed as the spectral or gradient approaches. In addition, a way to enhance the spectral method with the Radon transform is proposed. The aim of this document is to determine which method provides greatest results when the resolution grid is finer. Therefore, the methods accuracy, fidelity and uncertainty are compared through a simulation study, a section with RadarSAT2 data in coastal area and another one with Sentinel-1 measurements in offshore area.