As part of aircraft certification and optimization, wing bending and twist measurements are performed under various load cases (aircraft weight, speed, angle of attack, etc.) to validate and improve wing deformation models. Since these measurements are acquired during flight, their analysis requires to face strong environmental constraints. Indeed, the highly varying luminosity conditions, the presence of possible reflections or shadows, the vibrations and the deformations of the entire aircraft, are strong constraints that need to be considered carefully. Current approaches applied in Airbus are based on inertial measurement units installed inside the wing, or on photogrammetry-based solutions using calibrated sensors and retro-reflective targets located on the wings. These methods are not only highly intrusive, but also require time-consuming installation, calibration phases and dedicated flights to produce only sparse measurements. Moreover, the use of reflective targets on the wing has an impact on the wing aerodynamic, which should be avoided. In this paper, we investigate a new method for estimating wing deformations. This method adapts a photogrammetry approach classically used for reconstructing buildings or art structures to the aircraft environment. To this aim, we propose to use synchronous videos from high resolution cameras, which can be easily installed on the aircraft windows and on the vertical stabilizer. Appropriate features are extracted from the images acquired by these cameras, related to wing joints or reference points located on the aircraft wing. The system uses these features to autonomously recalibrate itself at each frame and provide a dense 3D reconstruction of the wing in the aircraft reference coordinate system. Some experiments conducted on real data acquired on Airbus aircrafts show that the proposed estimation method provide promising results.

Global Navigation Satellite Systems (GNSS) are the main source of position, navigation, and timing (PNT) information and will be a key player in the next-generation intelligent transportation systems and safety-critical applications, but several limitations need to be overcome to meet the stringent performance requirements. One of the open issues is how to provide precise PNT solutions in harsh propagation environments. Under nominal conditions, the former is typically achieved by exploiting carrier phase information through precise positioning techniques, but these methods are very sensitive to the quality of phase observables. Another option that is gaining interest in the scientific community is the use of large bandwidth signals, which allow obtaining a better baseband resolution, and therefore more precise code-based observables. Two options may be considered: (i) high-order binary offset carrier (HO-BOC) modulations or (ii) the concept of GNSS meta-signals. In this contribution, we assess the time-delay and phase maximum likelihood (ML) estimation performance limits of such signals, together with the performance translation into the position domain, considering single point positioning (SPP) and RTK solutions, being an important missing point in the literature. A comprehensive discussion is provided on the estimators’behavior, the corresponding ML threshold regions, the impact of good and bad satellite constellation geometries, and final conclusions on the best candidates, which may lead to precise solutions under harsh conditions. It is found that if the receiver is constrained by the receiver bandwidth, the best choices are the L1-M or E6-Public Regulated Service (PRS) signals. If the receiver is able to operate at 60 MHz, it is recommended to exploit the full-bandwidth Galileo E5 signal. In terms of robustness and performance, if the receiver can operate at 135 MHz, the best choice is to use the GNSS meta-signals E5 + E6 or B2 + B3, which provide the best overall performances regardless of the positioning method used, the satellite constellation geometry, or the propagation conditions.

Named Data Networking (NDN), an Information-Centric Network (ICN) architecture, is based on caching, multipath and multi-producers retrieving. These properties provide new opportunities for a single user to increase its Quality of Experience (QoE). However, handling multiple flows, each of them having its own multiple paths, is more complex. To tackle this challenge, we highlight three main principles a solution should include. Nodes should cooperate, supervise their output queues and, eventually, wisely manage the multipath capacities of NDN. These three elements are the core of our proposition : Cooperative Congestion Control (CCC). More than a solution, CCC is proposed as a framework where each principle could be implemented in multiple ways. The ultimate objective is to fairly distribute the flows on the network and maximize QoE of users. We choose basic algorithms in order to evaluate the overall framework. We evaluate our solution with simulations and compare their results with a theoretical model.

The derivation of tight estimation lower bounds is a key tool to design and assess the performance of new estimators. In this contribution, first, the authors derive a new compact Cramér–Rao bound (CRB) for the conditional signal model, where the deterministic parameter's vector includes a real positive amplitude and the signal phase. Then, the resulting CRB is particularised to the delay, Doppler, phase, and amplitude estimation for band-limited narrowband signals, which are found in a plethora of applications, making such CRB a key tool of broad interest. This new CRB expression is particularly easy to evaluate because it only depends on the signal samples, then being straightforward to evaluate independently of the particular baseband signal considered. They exploit this CRB to properly characterise the achievable performance of satellite-based navigation systems and the so-called real-time kinematics (RTK) solution. To the best of the authors’ knowledge, this is the first time these techniques are theoretically characterised from the baseband delay/phase estimation processing to position computation, in terms of the CRB and maximum-likelihood estimation.

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.