The release of Android GNSS raw measurements, in late 2016, unlocked the access of smartphones’ technologies for advanced positioning applications. Recently, smartphones’ GNSS capabilities were optimized with the release of multi-constellation and multi-frequency GNSS chipsets. In the last few years, several papers studied the use of Android raw data measurements for developing advanced positioning techniques such as Precise Point Positioning (PPP) or Real-Time Kinematic (RTK), and quantified those measurements compare to high-end commercial receivers. However, characterizing different smartphone models and chipset manufacturers in urban environment remains an unaddressed challenge. In this paper, a thorough data analysis will be conducted based on a data collection campaign that took place in Toulouse city center. Collaborative scenarios have been put in place while navigating in deep urban canyons. Two vehicles were used for this experiment protocol, equipped with high-end GNSS receivers for reference purposes, while seven smartphones were tested. Android algorithms reliability of both the multipath and cycle slip flags were investigated and evaluated as potential performance parameters. Our study suggests that their processing may differ from one brand to another, making their use as truthful quality indicators for collaborative positioning yet open to debate.

In previous works, new families of Pseudo-Random Noise (PRN) codes of length 1023 chips were proposed in order to ease the acquisition engine. These studies analyzed several metrics for code design in order to improve the acquisition but no analysis was conducted on the estimation performance, which in turn drives the final position, velocity and timing estimates. The main goal of this contribution is to assess if these new PRN codes designed to improve the acquisition engine lose in achievable time-delay estimation performance with respect to the standard GPS L1 C/A Gold codes. The analysis is performed by resorting to a new compact closed-form Cramér-Rao bound expression for time-delay estimation which only depends on the signal samples. In addition, the corresponding time-delay maximum likelihood estimate is also provided to assess the minimum signal-to-noise ratio that allows to be in optimal receiver operation.

This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data jointly, is intrinsically shift-invariant, and crucially, it encodes each input signal (either continuous or discrete) from a joint activation and uniform combinations of filters, allowing the correlation across the input signals to be captured. The performance of C-ADDICT, is evaluated on a representative dataset composed of real spacecraft telemetries with an available ground-truth, providing promising results.

This contribution analyzes the fundamental performance limits of traditional two-step Global Navigation Satellite System (GNSS) receiver architectures, which are directly linked to the achievable time-delay estimation performance. In turn, this is related to the GNSS baseband signal resolution, i.e., bandwidth, modulation, autocorrelation function, and the receiver sampling rate. To provide a comprehensive analysis of standard point positioning techniques, we consider the different GPS and Galileo signals available, as well as the signal combinations arising in the so-called GNSS meta-signal paradigm. The goal is to determine: (i) the ultimate achievable performance of GNSS code-based positioning systems; and (ii) whether we can obtain a GNSS code-only precise positioning solution and under which conditions. In this article, we provide clear answers to such fundamental questions, leveraging on the analysis of the Cramér–Rao bound (CRB) and the corresponding Maximum Likelihood Estimator (MLE). To determine such performance limits, we assume no external ionospheric, tropospheric, orbital, clock, or multipath-induced errors. The time-delay CRB and the corresponding MLE are obtained for the GPS L1 C/A, L1C, and L5 signals; the Galileo E1 OS, E6B, E5b-I, and E5 signals; and the Galileo E5b-E6 and E5a-E6 meta-signals. The results show that AltBOC-type signals (Galileo E5 and meta-signals) can be used for code-based precise positioning, being a promising real-time alternative to carrier phase-based techniques.

Spacecraft health monitoring and failure prevention are major issues in space operations. In recent years, machine learning techniques have received an increasing interest in many elds and have been applied to housekeeping telemetry data via semi-supervised learning. The idea is to use past telemetry describing normal spacecraft behaviour in order to learn a reference model to which can be compared most recent data in order to detect potential anomalies. This paper introduces a new machine learning method for anomaly detection in telemetry time series based on a sparse representation and dictionary learning. The main advantage of the proposed method is the possibility to handle multivariate telemetry time series described by mixed continuous and discrete parameters, taking into account the potential correlations between these parameters. The proposed method is evaluated on a representative anomaly dataset obtained from real satellite telemetry with an available ground-truth and compared to state-of-the-art algorithms.

This paper develops a Rao-Blackwellized particle filter with variational inference for jointly estimating state and time-varying parameters in non-linear state-space models (SSM) with non-Gaussian measurement noise. Depending on the availability of the conjugate prior for the unknown parameters, the joint posterior distribution of the state and unknown parameters is approximated by using an auxiliary particle filter with a probabilistic changepoint model. The distribution of the SSM parameters conditionally on each particle is then updated by using variational Bayesian inference. Experiments are first conducted on a modified nonlinear benchmark model to compare the performance of the proposed approach with other state-of-the-art approaches. Finally, in the context of GNSS multipath mitigation, the proposed approach is evaluated based on data obtained from a measurement campaign conducted in a street urban canyon.

This article investigates on the derivation of good log likelihood ratio (LLR) approximations under uncorrelated fading channels with partial statistical channel state information (CSI) at the receiver. While previous works focused mainly on solutions exploiting full statistical CSI over the normalized Rayleigh fading channel, in this article, a Bayesian approach based on conjugate prior analysis is proposed to derive LLR values that only uses moments of order one and two associated with the random fading coefficients. The proposed approach is shown to be a more robust method compared to the best existing approximations, since it can be performed independently of the fading channel distribution and, in most cases, at a lower complexity. Results are validated for both binary and M-ary modulations over different uncorrelated fading channels.

Hidden Markov models (HMM) have been widely used for sequence modeling, such as speech and proteins, where the sequential signal is modeled as a doubly stochastic process compound of a hidden sequence inferred from the observed one. HMM captures the temporal context of sequences through the model parameters. This work studies the anomaly detection problem in time series via the learning of HMM parameters from observable sequences. For this, the maximum likelihood estimation of normal sequences is used to learn the model that best characterizes the normal behavior of the observed signals. Then, the log-probability of test sequences is computed using the learned-HMM, where higher values indicate a high probability of being a normal sequence. As a case of study, the approach is applied to multitemporal remote sensing by using extracted indicators from 13 Sentinel-2 images of rapeseed crops. The detection performance is evaluated in terms of precision and recall, where the HMM-learning approach obtains comparable detection rates against classical anomaly detection methods.

The recent decades have seen an increasing interest in Medium Earth Orbit and Low Earth Orbit satellite constellations. However, there is little information on the delay variation characteristics of these systems and the resulting impact on high layer protocols. To fill this gap, this paper simulates a constellation that exhibits the same delay characteristics as the already deployed Iridium but considers closer bandwidths to constellation projects. We identify five major sources of delay variation in polar satellite constellations with different occurrence rates: elevation, intra-orbital handover, inter-orbital handover, orbital seam handover and Inter-Satellite Link changes. We simulate file transfers of different sizes to assess the impact of each of these delay variations on the file transfer. We conclude that the orbital seam is the less frequent source of delay and induces a larger impact on a small file transfers: the orbital seam, which occurs at most three times during 24 hours, induces a 66% increase of the time needed to transmit a small file. Inter-orbital and intra-orbital handovers occur less often and reduce the throughput by approximately ~ 8% for both low and high throughput configurations. The other sources of delay variations have a negligible impact on small file transfers, and long file transfers are not impacted much by the delay variations.

This paper introduces a new fusion method for magnetic resonance (MR) and ultrasound (US) images, which aims at combining the advantages of each modality, i.e., good contrast and signal to noise ratio for the MR image and good spatial resolution for the US image. The proposed algorithm is based on two inverse problems, performing a super-resolution of the MR image and a denoising of the US image. A polynomial function is introduced to model the relationships between the gray levels of the two modalities. The resulting inverse problem is solved using a proximal alternating linearized minimization framework. The accuracy and the interest of the fusion algorithm are shown quantitatively and qualitatively via evaluations on synthetic and experimental phantom data.