This letter introduces a new low-complexity frequency-domain equalizer for continuous phase modulations (CPM). The derivation of a fractionally spaced representation for circular block-based CPM leads, without any approximation, to a simple yet efficient frequency-domain equalization. The proposed equalizer is compared to the state-of-the-art approaches. Simulation results show the equivalence in terms of performance with a lower or similar complexity.

This paper studies a statistical approach to detect and diagnose a particular type of vibration impacting the control surfaces of civil aircraft. The considered phenomenon is called Limit Cycle Oscillation (LCO). It consists of an unwanted sustained oscillation of a control surface due to the combined effect of aeroelastic phenomena and an increased level of mechanical free play in the elements that connect the control surface to the aerodynamic surface. The stateof-the-art for LCO prevention is mainly based on regular free play checks performed on ground during maintenance operations. The detection is mainly achieved by the crew, and especially the pilot who can fill in a so-called “vibration reporting sheet” to describe the phenomena felt during the flight. Thus, the pilot sensitivity to vibration is still the only reference for LCO detection. In the Flight Control System (FCS) of modern aircraft there exist already several certified algorithms for the detection of vibrations of different nature, which use dedicated local sensors to monitor the control surface behaviour. The same kind of sensors have been chosen in a local approach, which eases the isolation of the vibration sources. This paper studies a new statistical approach based on the Generalized Likelihood Ratio Test (GLRT) in order to improve the state-of-the-art for LCO detection and diagnosis. The test and its theoretical performance are derived and validated. A straightforward method compliant with real-time implementation constraint for LCO prediction is proposed. A Monte Carlo test campaign is performed in order to assess the robustness and the detection/diagnosis performance of the proposed algorithm under different operating conditions.

In this paper, we consider single carrier continuous phase modulations (CPM) over frequency selective time-varying channels. In this context, we propose a new low-complexity frequency-domain equalizer based on the minimum mean square error (MMSE) criterion exploiting efficiently the band structure of the associated channel matrix in the frequency domain. Simulations show that this band-MMSE equalizer exhibits a good performance complexity trade-off compared to existing solutions.

A control logic has a central role in many echo cancellation systems for optimizing the performance of adaptive filters, while estimating the echo path. For reliable control, accurate double-talk and channel change detectors are usually incorporated to the echo canceler. This work expands the usual detection strategy to define a classification problem characterizing four possible states of the echo canceler operation. The new formulation allows the use of decision theory to continuously control the transitions among the different modes of operation. The classification rule reduces to a low-cost statistics, for which it is possible to determine the probability of error under all hypotheses, allowing the classification performance to be accessed analytically. Monte Carlo simulations using synthetic and real data illustrate the reliability of the proposed method.

Texture characterization of natural images using the mathematical framework of multifractal (MF) analysis, enables the study of the fluctuations in the regularity of image intensity. Although successfully applied in various contexts, the use of MF analysis has so far been limited to the independent analysis of a single image, while the data available in applications are increasingly multivariate. This paper addresses this limitation and proposes a joint Bayesian model and associated estimation procedure for MF parameters of multivariate images. It builds on a recently introduced generic statistical model that enabled the Bayesian estimation of MF parameters for a single image and relies on the following original key contributions : First, we develop a novel Fourier domain statistical model for a single image that permits the use of a likelihood that is separable in the MF parameters via data augmentation. Second, a joint Bayesian model for multivariate images is formulated in which prior models based on gamma Markov random fields encode the assumption of the smooth evolution of MF parameters between the image components. The design of the likelihood and of conjugate prior models is such that exploitation of the conjugacy between the likelihood and prior models enables an efficient estimation procedure that can handle a large number of data components. Numerical simulations conducted using sequences of multifractal images demonstrate that the proposed procedure significantly outperforms previous univariate benchmark formulations at a competitive computational cost.

Trellis-based detector is an effective method to demodulate non-coherent continuous-phase modulated sequences. Most of them have been derived for the maximum likelihood sequence estimation setting, while only few contributions have been proposed for the maximum a posteriori (MAP) symbol detection, required when soft information is needed for iterative detection and decoding. In this letter, we derive a new symbol MAP non-coherent receiver with reduced state space representation compared with the existing extended state-space-based approaches. While having the same performance, it enables a lower complexity.

Multipath remains the main source of error when using global navigation satellite systems (GNSS) in constrained environment, leading to biased measurements and thus to inaccurate estimated positions. This paper formulates the GNSS navigation problem as the resolution of an overdetermined system, which depends nonlinearly on the receiver position and linearly on the clock bias and drift, and possible biases affecting GNSS measurements. The extended Kalman filter is used to linearize the navigation problem whereas sparse estimation is considered to estimate multipath biases. We assume that only a part of the satellites are affected by multipath, i.e., that the unknown bias vector is sparse in the sense that several of its components are equal to zero. The natural way of enforcing sparsity is to introduce an `1 regularization associated with the bias vector. This leads to a least absolute shrinkage and selection operator (LASSO) problem that is solved using a reweighted-l1 algorithm. The weighting matrix of this algorithm is designed carefully as functions of the satellite carrier to noise density ratio and the satellite elevations. The smooth variations of multipath biases versus time are enforced using a regularization based on total variation. An experiment conducted on real data allows the performance of the proposed method to be appreciated.

The increase and reinforcement of Internet uses make necessary to improve existing networks. However, we observe strong inequalities between urban areas, well served and which concentrate the major part of investments, and rural areas, underserved and forkasen. To face this situation, users in underserved areas are moving to others Internet access, and in particular satellite Internet access. However, the latter suffer from a limitation which is the long delay induced by the propagation time between the earth and the geostationnary orbit. In this thesis, we are interresed in the simultaneous use of a terrestrial access network, characterized by a low delay and a low throughput, and a satellite access network, characterized by a high throughput and an long delay. Elsewhere, Content Delivery Networks (CDNs), consisting of a large number of cache servers, bring an answer to the increase in trafic and needs in terms of latency and throughput. However, located in core networks, cache servers stay far from end users and do not reach access networks. Thus, Internet Service Providers (ISPs) have taken an interest in deploying their own CDNs, which will be referred to as TelCo CDNs. The content delivery ideally needs the interconnection between CDN operators and TelCo CDNS, allowing the delegation of the content delivery to the TelCo CDNs. The latter are then able to optimize the content delivery on their network, for which they have a better knowledge. Thus, we will study the optimization of the contents delivery on a hybrid satellite / terrestrial network, integrated in a CDN delivery chain. We will initially focus on the description of a architecture allowing, thanks to a CDN interconnection, handling contents delivery on the hybrid network. In a second stage, we will study the value of the information provided by the CDN context in the routing on such architecture. In this framework, we will propose a routing mechanism based on contents size. Finally, we will show the superiority of our approach over the multipath transport protocol MP-TCP.

A new sparse estimation method was recently introduced in a previous work to correct biases due to multipath (MP) in GNSS measurements. The proposed strategy was based on the resolution of a LASSO problem constructed from the navigation equations using the reweighted-l1 method. This strategy requires to adjust the regularization parameters balancing the data fidelity term and the involved regularizations. This paper introduces a new Bayesian estimation method allowing the MP biases and the unknown model parameters and hyperparameters to be estimated directly from the GNSS measurements. The proposed method is based on BernoulliLaplacian priors, promoting sparsity of MP biases.