Multipath is an important source of error when using global navigation satellite systems (GNSS) in urban environment, leading to biased measurements and thus to false positions. This paper treats the GNSS navigation problem as the resolution of an overdetermined system, which depends on the receiver’s position, velocity, clock bias, clock drift, and possible biases affecting GNSS measurements. We investigate a sparse estimation method combined with an extended Kalman filter to solve the navigation problem and estimate the multipath biases. The proposed sparse estimation method assumes 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 l1 regularization ensuring that the bias vector has zero components. This leads to a least absolute shrinkage and selection operator (LASSO) problem, which is solved using a reweighted-l1 algorithm. The weighting matrix of this algorithm is defined as functions of the carrier to noise density ratios and elevations of the different satellites. Moreover, the smooth variations of multipath biases versus time are enforced using a regularization based on total variation. For estimating the noise covariance matrix, we use an iterative reweighted least squares strategy based on the so-called Danish method. The performance of the proposed method is assessed via several simulations conducted on different real datasets.

The Vostok ice core provides measurements of the CO 2 concentration during the last 414 × 10 3 years (yr). Estimations of power spectra show peaks, with the strongest one corresponding to a time period of around 100 × 10 3 yr. In this paper, a new reconstruction method from irregular sampling is used, allowing more accurate estimation of spectral peaks. This method intrinsically decomposes the analyzed signal as a sum of sines, providing amplitudes but also phase measurements of periodic tendencies (due to the nature of the studied phenomena). This decomposition can be conducted with noisy and inaccurate measurements of the sampling instants and the concentrations. The widely used Vostok data were chosen as an example, but the method could also be applied to data from other places (e.g., dome C, Antarctica) or to study other phenomena as nitrogen dioxide NO 2 , methane CH 4 , oxygen isotope 18 O (closely linked to temperature), deuterium 2 H, or dust concentrations.

Satellite transmissions can suffer from high channel impairments, especially on the link between a satellite and a mobile end-user. To cope with these errors, physical and link layer reliability schemes have been introduced at the price of an end-to-end delay increase resulting in high jitter. Unfortunately, both the delay and the jitter negatively impacts on multimedia traffic. As a matter of fact, not taking into account the channel state greatly decreases the Quality of Experience (QoE) of VoIP users. In this paper, we propose to solve this issue by scheduling data transmission as a function of the channel condition. We first investigate existing scheduling mechanisms and analyze their performance for VoIP traffic with the objective to lower both latency and jitter, which are the most important metrics to achieve a consistent VoIP service. We select the best candidate among several schedulers and propose a novel algorithm specifically designed to carry VoIP over LEO constellations. Our simulations show that in some scenarios, we double the QoE of VoIP users.

In this paper we propose a new random access (RA) channel technique for the return link of satellite communications. It concerns slotted transmissions. This proposed method called Shared POsition Technique for Interfered random Transmissions (SPOTiT), is based on a shared knowledge between the receiver and each of the terminals. The shared information is about the time slot locations on which the terminal transmits its replicas as well as the preamble to use. The presented random version of SPOTiT aims to reduce the complexity of replicas localization process of the legacy technique Multireplica Decoding using Correlation based Localisation (MARSALA). It presents a less complex system without degrading performance and with no extra signaling information. Thus, SPOTiT is applied at the same level as MARSALA, i.e. when Contention Resolution Diversity Slotted Aloha (CRDSA) fails in retrieving more packets. This technique combined with CRDSA significantly reduces the number of data localization correlations, while maintaining the same performance as in CRDSA/MARSALA in terms of packet loss ratio and throughput.

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.