International audienceWe present in this survey new technologies proposed for the evolution of the aeronautical communication infrastructure. Motivated by studies that estimate the growth of air traffic flow, it was decided to develop a future communication infrastructure (FCI) adapted to the future aeronautical scenario. The FCI development involves researchers, industrials, and aeronautical authorities from many countries around the world, and started in 2004. The L-band Digital Aeronautical Communication System (L-DACS) is the part of the FCI that will be in charge of continental communication. The L-DACS is being developed in Europe since 2007 and two candidates were preselected: L-DACS1 and L-DACS2. In this paper, we first describe the motivations of the FCI. We then give an overview of its development activities from 2004 to 2009. After that, we provide some insights about both preselected L-DACS candidates, at their physical and medium access layers. Finally, we address the challenges on the development of the FCI/L-DACS

Detection and localization in urban environments is a very recent radar problem. In this article, we investigate the possibility of detecting and locating targets in NLOS areas with a single portable radar by exploiting multipath returns. We propose two algorithms which handle the information provided by multipath returns in different ways to detect and estimate the NLOS target position. We also present an original method to select the number of paths to take into account in the algorithms in order to maximize detection probabilities. Numerical results show good efficiency of the proposed algorithms for problems of both detection and localization. We show that applying these algorithms improves detection performance compared to a classic matched filter in a typical urban scenario. Experimental results on a real data set allow us to validate our multipath model in urban environments, and in particular to show that it is possible to retrieve the target location even with rough knowledge of the scene geometry.

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Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting conditions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.