Precise positioning (based on Precise Point Positioning, PPP, or Real Time Kinematics, RTK) is steadily gaining momentum. The main difficulty when using carrier phase measurements remains to correctly estimate their ambiguities: not only is it a computationally intensive process, but it can be affected by cycle slips (CS), which are brutal variations in ambiguity values, due to receiver’s dynamics or unfortunate reception events. As GNSS constellations are now able to provide users with signals on three different frequencies, the concept of Triple Carrier Ambiguity Resolution has become widespread. It typically relies on the use of widelane signals, which are combinations of raw signals and are defined as to have an apparent wavelength much higher than original signals, thus making accelerating the ambiguity fixing process and reducing the frequency of cycle slips. However, CS may remain a problem for the availability of precise positioning services. The present paper therefore focuses on a cycle slip detection method, based on a hypothesis test. The main idea consists in using both code and widelane phase measurements to compute a geometry- and ionospheric-free test vector, theoretically containing only noise and possible cycle slips. The latter can be detected by looking for brutal changes on the average of the test vector. Performance is assessed on simulated and Rinex data.

In the recent times, the interest of reducing the time to retrieve the Clock and Ephemerides Data (CED) has provided an open subject of study to design the structure of the message along with the channel coding scheme of the GNSS signals. As a direct consequence, a new methodology to co-design the navigation message and the channel coding scheme structure is proposed in this paper. This new co-design enables both to reduce the time to retrieve the CED and enhanced error correction capabilities under degraded channel conditions. In order to accomplish such as requirements, codes, which provide both maximum distance separable and full diversity properties under the non-ergodic channel assumption, are designed.

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.