The objective of this paper is to determine the ranging performance of the upcoming fifth generation (5G) signal. In order to do so, it is required to define 5G correlator outputs mathematical models. 5G systems will use OFDM (Orthogonal Frequency Division Multiplexing) signals; in the literature, mathematical models of OFDM signals are developed at the different receiver signal processing stages. These models assumed that the propagation channel is constant over an OFDM symbol; nevertheless, an in-depth study of QuaDRiGa, a 5G compliant propagation channel simulator, invalidates this hypothesis. Therefore, in this paper, mathematical models are developed that take into account the channel evolution. The focus is given on correlator outputs and results are applied to the computation of 5G based pseudo range accuracy.

Error correcting schemes are fundamental in the new generation of data navigation signals. Thanks to those, the system has the capability to correct possible data navigation errors, which potentially induces delays in first fix of the receiver. In the GNSS receivers, those error correcting schemes use the Log Likelihood Ratio (LLR) as the input of the decoding algorithm. Until now, the LLR was always computed under the Gaussian assumption and considering perfect Complete State Information (CSI), which does not hold in most of the real scenarios. Then, in this paper we proposed several methods to compute the LLR, considering a set of realitic scenarios and considering that perfect CSI is not available at the receiver. We test the proposed LLRs for several new generation GNSS signals.

New generation of GNSS systems seeks to provide new features in order to create or to improve their currents services. Between those possible features; the increase of the data rate is necessary in order to to provide services such as authentication, precise positioning or reduce the Time-To-First-Fix (TTFF). On the other hand, the data availability in harsh environment suggest the need of error correcting technologies. Then, based on previous works over the Code-Shift Keying (CSK) modulation and in Root Protograph LDPC code to reduce the TTFF, in this paper, it is presented the optimization of Root Protograph LDPC codes for the CSK modulation in a Bit-Interleaved Coded Modulation context and the optimization of Root Protograph LDPC codes for the CSK modulation in Bit-Interleaved Coded Modulation Iterative Decoding context. Both optimization where base on the Protograph EXIT chat algorithm, providing promising results.

In this article, we provide the method to construct two error correcting structures for GNSS systems, which are capable to provide Maximum Distance Separable (MDS), full diversity and rate-compatible properties. Thanks to those properties, the GNSS receiver is capable to reduce the Time-To-First-Fix (TTFF) and to enhance the robustness of the data demodulation under low Carrier to Noise ratio environments, urban environments and pulsed jamming environments. The proposed error correcting structures are then simulated and compared with the GPS L1C subframe 2 error correcting scheme under the precedent transmission environments. Simulations show an outstanding improvement of the error correction capabilities (which reduce the TTFF in harsh environments) mainly caused by the rate-compatible and the full diversity properties. Moreover, thanks to the MDS property a high reduction of the TTFF under good environments is appreciated.

For the sake of Air Traffic Management modernization, civil aviation organizations are currently developing IPS for Aeronautical Safety Services in the new ATN/IPS infrastructure. This includes to define new airborne and ground- based communication systems capable of managing both air traffic services (ATS) and aeronautical operational communications (AOC) safety services. One of the main challenges in this new ATN/IPS network is the IPv6 mobility problem. This paper proposes a solution which takes both advantages of ground based Locator/Identifier Separation Protocol and Proxy Mobile IPv6 to manage all the aircraft mobility scenarios. A dedicated OMNeT ++ simulation model is also provided and shows the performances of our solution.

This paper studies a new motion estimation method based on convolutional sparse coding. The motion estimation problem is formulated as the minimization of a cost function composed of a data fidelity term, a spatial smoothness constraint, and a regularization based on convolution sparse coding. We study the potential interest of using a convolutional dictionary instead of a standard dictionary using specific examples. Moreover, the proposed method is evaluated in terms of motion estimation accuracy and compared with state-of-the-art algorithms, showing its interest for cardiac motion estimation.

Single-photon light detection and ranging (Lidar) devices can be used to obtain range and reflectivity information from 3D scenes. However, reconstructing the 3D surfaces from the raw waveforms can be very challenging, in particular when the number of spurious background detections is large compared to the number of signal detections. This paper introduces a new and fast detection algorithm, which can be used to assess the presence of objects/surfaces in each waveform, allowing only the histograms where the imaged surfaces are present to be further processed. The method is compared to state-of-the-art 3D reconstruction methods using synthetic and real single-photon data and the results illustrate its benefits for fast and robust target detection using single-photon data.

Cet article présente un modèle de représentation parcimonieuse pondérée pour la détection d'anomalies dans des signaux de télémesure satellite multivariés. La méthode proposée est une extension de l'état de l'art par son adaptation au cadre multivarié et l'intégration d'informations externes par l'intermédiaire d'une pondération appropriée permettant d'améliorer les performances de détection.

For linear discrete state-space models, under certain conditions, the linear least mean squares (LLMS) filter estimate has a recursive format, a.k.a. the Kalman filter (KF). Interestingly, the linear minimum variance distortionless response (LMVDR) filter, when it exists, shares exactly the same recursion as the KF, except for the initialization. If LMVDR estimators are suboptimal in mean-squared error sense, they do not depend on the prior knowledge on the initial state. Thus, the LMVDR estimators may outperform the usual LLMS estimators in case of misspecification of the prior knowledge on the initial state. In this perspective, we establish the general conditions under which existence of the LMVDRF is guaranteed. An immediate benefit is the introduction of LMVDR fixed-point and fixed-lag smoothers (and possibly other smoothers or predictors), which has not been possible so far. Indeed, the LMVDR fixed-point smoother can be used to compute recursively the solution of a generalization of the deterministic least-squares problem.