Positioning and navigation by GNSS in urban context are always challenging tasks, because of signal propagation problems such as shadowing effects and multipath. When not enough GNSS signals are received in line-of-sight (LOS), classical approaches mitigating multipath effects become insufficient because there is not enough reliable information available. Consequently, positioning errors can be about tens of meters, especially in urban canyons. In this paper, we introduce a NSS positioning approach that uses constructively non-line-of-sight (NLOS) signals in order to have enough information to compute the user’s position. In this work, we use the SE-NAV software to predict the geometric paths of NLOS signals using a high realistic 3D model of the environment. More precisely, we propose a new version of the extended Kalman filter augmented by the information provided by SE-NAV, referred to as 3D AEKF, for GNSS navigation in NLOS context. In the proposed approach, the measurement model traditionally based on the trilateration equations is constructed from the received paths estimated by SENAV. The Jacobian of the measurement model is calculated through knowledge of the objects on which the reflections have occured. To use even less reliable measurements, we propose a robust version of the 3D AEKF. Simulations conducted in realistic scenarios allow the performance of the proposed method to be evaluated.

Increased sensitivity and reduced fast time to first fix (TTFF) are key performance indicators for GNSS receivers, which depend on the surrounding environment, receiver design and available aiding information. To reduce the effect of attenuations, dynamics and navigation data bits transition, the GNSS acquisition engines employ both coherent and post-coherent signal integration strategies, namely non-coherent and differentially coherent. Understanding the advantages and drawbacks of each post-coherent integration strategy is fundamental in the process of optimization of the acquisition scheme, as well as the effect of high-dynamics in both coherent and post-coherent operations. In this paper, we study three important issues of GNSS acquisition. First, we propose a closed form expression to quantify the effect of a linearly changing Doppler frequency on the coherent integration output. Second, we derive a formula capable of characterizing the sensitivity gain of a differential integration detector. Third, we compare the effect of dynamics on both non-coherent and differential integration. The main objective of this work is to combine these three contributions for overall optimization of the acquisition scheme. More precisely, we mitigate the effect of large Doppler errors without compromising receiver’s sensitivity and ideally without additional computational cost.

We present a solution to evaluate the performance of transport protocols as a function of link layer reliability schemes (i.e. ARQ, FEC and Hybrid ARQ) applied to satellite physical layer traces. As modelling such traces is complex and may require approximations, the use of real traces will minimise the potential for erroneous performance evaluations resulting from imperfect models. Our Trace Manager Tool (TMT) produces the corresponding link layer output, which is then used within the ns-2 network simulator via the additionally developed ns-2 interface module. We first present the analytical models for the link layer with bursty erasure packets and for the link layer reliability mechanisms with bursty erasures. Then, we present details of the TMT tool and our validation methodology, demonstrating that the selected performance metrics (recovery delay and throughput efficiency) exhibit a good match between the theoretical results and those obtained with TMT. Finally, we present results showing the impact of different link layer reliability mechanisms on the performance of TCP Cubic transport layer protocol.

We propose a novel DTN routing algorithm, called DQN, specifically designed for quasi-deterministic networks with an application to satellite constellations. We demonstrate that our proposal efficiently forwards the information over a satellite network derived from the Orbcomm topology while keeping a low replication overhead. We compare our algorithm against other well-known DTN routing schemes and show that we obtain the lowest replication ratio without the knowledge of the topology and with a delivery ratio of the same order of magnitude than a reference theoretical optimal routing.

This paper addresses the problem of error correction in a multi-user trellis coded system in the presence of bit stuffing. In particular, one considers the situation in which automatic identification system (AIS) signals are received by a satellite. The proposed receiver uses a cyclic redundancy check (CRC) for error correction. A Viterbi algorithm based on a so-called extended trellis is developed. This trellis is defined by extended states composed of a trellis-code state and a CRC state. Moreover, special conditional transitions are defined in order to take into account the possible presence of bit stuffing. The proposed algorithm was first developed in a single-user context. It is generalized in this paper to a multi-user scenario by designing an interference mitigation method. This method allows one to derive a demodulation algorithm whose complexity is almost identical to that obtained in the single-user context. Some performance results are presented in the context of AIS and compared with results provided by existing techniques.

The delineation of P and T waves is important for the interpretation of ECG signals. In this work, we propose a sequential Bayesian detection-estimation algorithm for simultaneous P and T wave detection, delineation, and waveform estimation on a beat-to-beat basis. Our method is based on a dynamic model which exploits the sequential nature of the ECG by introducing a random walk model to the waveforms. The core of the method is a marginalized particle filter that efficiently resolves the unknown parameters of the dynamic model. The proposed algorithm is evaluated on the annotated QT database and compared with state-of-the-art methods. Its on-line characteristic is ideally suited for real-time ECG monitoring and arrhythmia analysis.

Global aircraft optimization is a main concern for future and upcoming programs. In particular, great research efforts are dedicated to Electrical Flight Control Systems (EFCS). Obviously, their reliability increases with the redundancy of the flight parameter sensors. However, physical redundancy, obtained by increasing the number of sensors, penalizes the aircraft weight and cost. This paper proposes a sensor failure detection method based on analytic redundancy. The flight parameter of interest is modelled as a linear function of independent sensor measurements on a sliding observation window. The Partial Least Squares (PLS) algorithm is used to estimate regression coefficients on this window. The PLS computes the solution via an iterative processing, and thus can be implemented in the flight control computer for a real time use. Two different failure detection strategies based on the behaviour of the regression coefficients are proposed. Simulation results show that the proposed method leads to robust detections.

In numerous applications, it is required to estimate the principal subspace of the data, possibly from a very limited number of samples. Additionally, it often occurs that some rough knowledge about this subspace is available and could be used to improve subspace estimation accuracy in this case. This is the problem we address herein and, in order to solve it, a Bayesian approach is proposed. The main idea consists of using the CS decomposition of the semi-orthogonal matrix whose columns span the subspace of interest. This parametrization is intuitively appealing and allows for non informative prior distributions of the matrices involved in the CS decomposition and very mild assumptions about the angles between the actual subspace and the prior subspace. The posterior distributions are derived and a Gibbs sampling scheme is presented to obtain the minimum mean-square distance estimator of the subspace of interest. Numerical simulations and an application to real hyperspectral data assess the validity and the performances of the estimator.

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent Þnite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images.