Global navigation satellite systems (GNSS) are a key player in a plethora of applications, ranging from navigation and timing, to Earth observation or space weather characterization. For navigation purposes, interference scenarios are among the most challenging operation conditions, which clearly impact the maximum likelihood estimates (MLE) of the signal synchronization parameters. While several interference mitigation techniques exist, a theoretical analysis on the GNSS MLE performance degradation under interference, being fundamental for system/receiver design, is a missing tool. The main goal of this contribution is to provide such analysis, by deriving closed-form expressions of the misspecified Cramér-Rao (MCRB) bound and estimation bias, for a generic GNSS signal corrupted by an interference. The proposed bias and MCRB expressions are validated for a linear frequency modulation chirp signal interference.

In this work, we investigate the problem of neuralbased error correction decoding, and more specifically, the new so-called syndrome-based decoding technique introduced to tackle scalability in the training phase for larger code sizes. We improve on previous works in terms of allowing full decoding of the message rather than codewords, allowing thus the application to nonsystematic codes, and proving that the single-message training property is still viable. The suggested system is implemented and tested on polar codes of sizes (64,32) and (128,64), and a BCH of size (63,51), leading to a significant improvement in both Bit Error Rate (BER) and Frame Error Rate (FER), with gains between 0.3dB and 1dB for the implemented codes in the high Signal-to-Noise Ratio (SNR) regime.

In this article, we first derive a general intrinsic Barankin bound (IBB) for unknown parameters lying on Lie groups (LGs), and its intrinsic McAulay-Seidman bound (IMSB) approximation. Second, the IMSB expression is used to revisit the intrinsic Cramér-Rao bound (ICRB) on LGs. Indeed, an analytic expression of the ICRB, which is a special IMSB case, is obtained from the latter. Finally, closed-form expressions for both IMSB and ICRB are obtained for Euclidean and LG observation models depending on parameters lying in SO(3) and SE(3). The validity of the these IMSB and ICRB expressions, with respect to the intrinsic mean square error, is shown via numerical simulations to support the discussion.

The study of ground reflections of Global Navigation Satellite System (GNSS) signals, as in GNSS Reflectometry (GNSS-R) can lead to the receiver height estimation. The latter is estimated by comparing the time of arrival difference between the direct and reflected signals, also called path separation. In ground-based scenarios, this path separation can be very small, inducing important interference between paths, which makes it difficult to correctly obtain altimetry products. The path separation estimation can be obtained by a brute force dual source maximum likelihood estimator (2S-MLE), but this solution has a large computational cost. On the other hand, the path separation is so small that a number of approximations can be done. In this study, a third order Taylor approximation of the dual source likelihood criterion is proposed to reduce its complexity. The proposed algorithm performance is compared to the non approximated 2S-MLE for the estimation of the path separation, and to a standard single source processing for the estimation of the direct signal time-delay. These results, along with the corresponding lower bounds, prove that the proposed approach may be of interest for two applications: ground-based GNSS-R altimetry (or radar with low elevation targets) and GNSS multipath mitigation.

This paper investigates a general framework for the registration of 3D magnetic resonance (MR) and 2D ultrasound (US) images. This framework is divided into a rigid slice-tovolume 3D-2D MR/US registration and a 2D-2D US/MRI fusion algorithm to generate an image having a better resolution than the MR image and a better contrast than the US image. The accuracy of the joint registration and fusion method is analyzed by means of quantitative and qualitative tests conducted on experimental phantom and realistic synthetic data generated from an in vivo MRI volume, with a specific attention to endometriosis treatment.

Interferences are an important threat for applications relying on Global Navigation Satellite Systems (GNSS). Interferences degrade GNSS performance, and can lead to denial of service. The most notable intentional interference family is characterized by its constant envelope, e.g. chirp and tone interferences. Due to its simple structure, the space to search the interference contribution yields to complex circles, allowing the introduction of some latent variables related to those circles. In order to mitigate the interference effect, we compute the maximum likelihood estimator of the parameters of interest (time delay and Doppler shift) in presence of those latent variables. Thus, we resort to the Expectation Maximization algorithm which has already been proved to be efficient in such cases. Experiments conducted on synthetic signals highlight the efficiency of the proposed algorithm.

In this article, we investigate on a new method to jointly design the navigation message with an error correcting scheme. This joint design exploits the "carousel" nature of the broadcasted navigation message and allows both: i) to reduce the Time To First Fix (TTFF) and ii) to enhance the error correcting performances under favorable and challenging channel conditions. We show that the joint design requires error correcting schemes characterized by Maximum Distance Separable (MDS) and the full diversity properties. Those error correcting codes are referred to as Root LowDensity Parity Check (Root-LDPC) codes and they can efficiently operate on block varying channels, enabling the efficient and rapid recovery of information over possibly non ergodic channels. Finally, in order to ensure the data demodulation performance over harsh condition, we propose Root-LDPC codes endowed with the nested property, which allows to inherently adapt the channel coding rate depending on the number of received blocks. The proposed error correcting joint design is then simulated and compared with the well-known GPS L1C subframe 2 structure under several transmission scenarios. Simulations showthatwe can have some enhancement of the error correction performance and a reduction of the TTFF for some scenarios.

Cet article présente un nouvel algorithme de recalage d’images par résonance magnétique (IRM) 3D et d’images échographiques (US) 2D. Le recalage prend en compte une transformation rigide globale caractérisée par des paramètres de rotation et de translation qui est associée à une déformation locale basée sur des fonctions B-splines. Un algorithme de fusion 2D-2D US-IRM est ensuite appliqué pour générer l’image finale qui contient les principales caractéristiques des deux images d’origine. La performance de la méthode de recalage est analysée au moyen de tests quantitatifs et qualitatifs effectués sur un ensemble de données réelles, avec une attention particulière au traitement de l’endométriose.

Dans cette communication, nous dérivons une nouvelle borne de Cramér-Rao intrinsèque pour paramètres et observations vivant sur groupes de Lie. L'expression est obtenue en utilisant les propriétés inhérentes à leur structure. De plus, une expression exacte est obtenue dans le cas où paramètres et observations sont sur SO(2), le groupe de Lie des matrices de rotations en 2D. La borne proposée est alors validée numériquement pour un modèle gaussien définie sur le groupe de Lie SO(2).

La détection d’anomalies dans le domaine de la surveillance maritime est un enjeu majeur pour la sécurité des navires et des nations. De nombreux algorithmes de détection d’anomalies sont disponibles dans la littérature mais ils ne sont pas tous adaptés à l’analyse de séries temporelles comme les trajectoires de navires, en particulier lorsque ces trajectoires n’ont pas la même longueur. Cet article présente une manière d’adapter l’algorithme One-Class SVM à la détection d’anomalies dans des séries temporelles associées à des trajectoires de navires à l’aide d’un noyau basé sur une mesure de similarité de type “dynamic time warping”.