Delay/Doppler altimetry (DDA) aims at reducing the measurement noise and increasing the along-track resolution in comparison with conventional pulse-limited altimetry. In a previous paper, we have proposed a semi-analytical model for DDA, which considers some simplifications as the absence of mispointing antenna. This paper first proposes a new analytical expression for the flat surface impulse response (FSIR), considering antenna mispointing angles, a circular antenna pattern, no vertical speed effect, and uniform scattering. The 2-D delay/Doppler map is then obtained by a numerical computation of the convolution between the proposed analytical function, the probability density function of the heights of the specular scatterers, and the time/frequency point target response of the radar. The approximations used to obtain the semi-analytical model are analyzed, and the associated errors are quantified by analytical bounds for these errors. The second contribution of this paper concerns the estimation of the parameters associated with the multilook semi-analytical model. Two estimation strategies based on the least squares procedure are proposed. The proposed model and algorithms are validated on both synthetic and real waveforms. The obtained results are very promising and show the accuracy of this generalized model with respect to the previous model assuming zero antenna mispointing.

We consider the problem of estimating the absolute phase of a noisy signal when this latter consists of correlated dual-frequency measurements. This scenario may arise in many application areas such as global navigation satellite system (GNSS). In this paper, we assume a slow varying phase and propose accordingly a Bayesian filtering technique that makes use of the frequency diversity. More specifically, the method results from a variational Bayes approximation and belongs to the class of nonlinear filters. Numerical simulations are performed to assess the performance of the tracking technique especially in terms of mean square error and cycle-slip rate. Comparison with a more conventional approach, namely a Gaussian sum estimator, shows substantial improvements when the signal-to-noise ratio and/or the correlation of the measurements are low.

In this paper, we assess the potential of several forms of the postcoherent differential detectors for the detection of weak Global Navigation Satellite Systems (GNSS) signals. We analyze in detail two different detector forms, namely the pairwise differential (PWD) and noncoherent differential (NCDD) detectors. First, we follow a novel approach to obtain analytic expressions to characterize statistically the PWD detector. Then, we use these results to propose a polynomial-like model fitted by simulation to the sensitivity loss experienced by the differential operation with respect to coherent summing. This sensitivity loss formulai s also used to characterize the NCDD detector, shown to be more adequate than the PWD for the acquisition of GNSS signals. A comparison between the PWD, NCDD and the traditional noncoherent detector (NCD) is also carried in this study. The results highlight the superior performance of the NCDD over the NCD for the acquisition of weak signals. For the case of the PWD, its performance is sensitive to Doppler shift. The conclusions drawn from the simulation results are confirmed in the acquisition of real GPS L1 C/A signals.

A statistical model for detecting changes in remote sensing images has recently been proposed in (Prendes et al., 2014, 2015). This model is sufficiently general to be used for homogeneous images acquired by the same kind of sensors (e.g., two optical images from Pléiades satellites, possibly with different acquisition conditions), and for heterogeneous images acquired by different sensors (e.g., an optical image acquired from a Pléiades satellite and a synthetic aperture radar (SAR) image acquired from a TerraSAR-X satellite). This model assumes that each pixel is distributed according to a mixture of distributions depending on the noise properties and on the sensor intensity responses to the actual scene. The parameters of the resulting statistical model can be estimated by using the classical expectation-maximization algorithm. The estimated parameters are finally used to learn the relationships between the images of interest, via a manifold learning strategy. These relationships are relevant for many image processing applications, particularly those requiring a similarity measure (e.g., image change detection and image registration). The main objective of this paper is to evaluate the performance of a change detection method based on this manifold learning strategy initially introduced in (Prendes et al., 2014, 2015). This performance is evaluated by using results obtained with pairs of real optical images acquired from Pléiades satellites and pairs of optical and SAR images.

Global Navigation Satellite Systems (GNSS) are increasingly present in our everyday life. New users are emerging with further operational needs implying a constant evolution of the current GNSS systems. A significant part of the new applications are found in environments with difficult reception conditions such as urban areas, where there are many obstacles such as buildings or trees. Therefore, in these obstructed environments, the signal emitted by the satellite is severely degraded. The signal received by the user has suffered from attenuations, as well as refractions and diffractions, making difficult the data demodulation and the user position calculation. GNSS signals being initially designed in an open environment context, their demodulaton performance is thus generally studied in the associated AWGN propagation channel model. But nowadays, GNSS signals are also used in degraded environments. It is thus essential to provide and study their demodulation performance in the urban propagation channel models. Nevertheless, GNSS modernization with new signals design such as GPS L1C or Galileo E1 OS, takes into account these new obstructed environments constraints. Since they have been designed especially for urban propagation channels [1], they are expected to have better demodulation performance compared with current GNSS signals. It is thus particularly interesting to firstly provide their demodulation performance in urban environments (not available in the literature) and secondly to compare it with the new GNSS signal designed in this PhD research context. In this way, their performance could be used as a benchmark for the future signals design. However these modernized signals are not yet available for this moment (for example, GPS L1C is expected to be operational over the entire constellation in 2026). It is thus essential to provide and study their demodulation performance in urban environments through simulations. It is in this context that this PhD thesis is related, the final goal being to improve GNSS signals demodulation performance in urban areas, proposing a new signal. In order to be able to provide and study GNSS signals demodulation performance in urban environments, a simulation tool has been developed : SiGMeP for "Simulator for GNSS Message Performance". It allows simulating the entire emission/reception GNSS signal chain in urban environment getting away from dependence of real signals availability, controlling the simulation parameters and testing new configurations. Existing and modernized signals demodulation performance has thus been computed with SiGMeP in urban environments. Since the classical way to compute and represent GNSS signals demodulation performance assumes an AWGN propagation channel model, and since the urban environments are really different from the AWGN channel, this classical method is not satisfactory in our urban context. Thus, in order to represent GNSS signals demodulation performance faithfully to reality, a new methodology more adapted to the user environment is proposed. It is based on the fundamental characteristics of a GNSS system, as well as on the urban environment impact on the received signal analysis. GNSS signals demodulation performance is thus provided in urban environments thanks to this new methodology, and compared with the classic methodology used in the AWGN case. Then, to improve GNSS signals demodulation performance in urban environments, many strategies are possible. However, the research axis on this thesis focuses on the "Channel Coding" aspect. It is thus this field which will be privileged to improve GNSS signals demodulation performance in urban environments. Each message, in addition to containing the useful information, carries redundant informaton, which is in fact the channel coding result, applying to the useful information. The message thus needs to be decoded at the reception. In order to decode the transmitted useful information, the receiver computes a detection function at the decoder imput. But the detection function used in classic receivers correspond to an AWGN propagation channel. This dissertation thus proposes an advanced detection function which is adapting to the propagation channel where the user is moving. This advanced detection function computation considerably improves demodulation performance, just in modifying the receiver part system. Finally, in order to design a new signal with better demodulation performance in urban environments than one of existing and future signals, a new LDPC channel code profile has been proposed, optimizing for a CSK modulation in an AWGN channel for iterative decoding. Indeed, the CSK modulation is a promising modulation in the spread signals world, which permits to free from limitations in terms of data rate implied by current GNSS signals modulations. Moreover, LDPC codes belong to the modern codes family, the first being able to approach the channel capacity.They thus represent promising achievable performance.

Global Navigation Satellite Systems (GNSS) are increasingly present in our everyday life. New users are emerging with further operational needs implying a constant evolution of the current GNSS systems. A significant part of the new applications are found in environments with difficult reception conditions such as urban areas, where there are many obstacles such as buildings or trees. Therefore, in these obstructed environments, the signal emitted by the satellite is severely degraded. The signal received by the user has suffered from attenuations, as well as refractions and diffractions, making difficult the data demodulation and the user position calculation. GNSS signals being initially designed in an open environment context, their demodulaton performance is thus generally studied in the associated AWGN propagation channel model. But nowadays, GNSS signals are also used in degraded environments. It is thus essential to provide and study their demodulation performance in the urban propagation channel models. Nevertheless, GNSS modernization with new signals design such as GPS L1C or Galileo E1 OS, takes into account these new obstructed environments constraints. Since they have been designed especially for urban propagation channels [1], they are expected to have better demodulation performance compared with current GNSS signals. It is thus particularly interesting to firstly provide their demodulation performance in urban environments (not available in the literature) and secondly to compare it with the new GNSS signal designed in this PhD research context. In this way, their performance could be used as a benchmark for the future signals design. However these modernized signals are not yet available for this moment (for example, GPS L1C is expected to be operational over the entire constellation in 2026). It is thus essential to provide and study their demodulation performance in urban environments through simulations. It is in this context that this PhD thesis is related, the final goal being to improve GNSS signals demodulation performance in urban areas, proposing a new signal. In order to be able to provide and study GNSS signals demodulation performance in urban environments, a simulation tool has been developed : SiGMeP for "Simulator for GNSS Message Performance". It allows simulating the entire emission/reception GNSS signal chain in urban environment getting away from dependence of real signals availability, controlling the simulation parameters and testing new configurations. Existing and modernized signals demodulation performance has thus been computed with SiGMeP in urban environments. Since the classical way to compute and represent GNSS signals demodulation performance assumes an AWGN propagation channel model, and since the urban environments are really different from the AWGN channel, this classical method is not satisfactory in our urban context. Thus, in order to represent GNSS signals demodulation performance faithfully to reality, a new methodology more adapted to the user environment is proposed. It is based on the fundamental characteristics of a GNSS system, as well as on the urban environment impact on the received signal analysis. GNSS signals demodulation performance is thus provided in urban environments thanks to this new methodology, and compared with the classic methodology used in the AWGN case. Then, to improve GNSS signals demodulation performance in urban environments, many strategies are possible. However, the research axis on this thesis focuses on the "Channel Coding" aspect. It is thus this field which will be privileged to improve GNSS signals demodulation performance in urban environments. Each message, in addition to containing the useful information, carries redundant informaton, which is in fact the channel coding result, applying to the useful information. The message thus needs to be decoded at the reception. In order to decode the transmitted useful information, the receiver computes a detection function at the decoder imput. But the detection function used in classic receivers correspond to an AWGN propagation channel. This dissertation thus proposes an advanced detection function which is adapting to the propagation channel where the user is moving. This advanced detection function computation considerably improves demodulation performance, just in modifying the receiver part system. Finally, in order to design a new signal with better demodulation performance in urban environments than one of existing and future signals, a new LDPC channel code profile has been proposed, optimizing for a CSK modulation in an AWGN channel for iterative decoding. Indeed, the CSK modulation is a promising modulation in the spread signals world, which permits to free from limitations in terms of data rate implied by current GNSS signals modulations. Moreover, LDPC codes belong to the modern codes family, the first being able to approach the channel capacity.They thus represent promising achievable performance.

Microsoft Kinect which has been primarily aimed at the computer gaming industry has been used in bio-kinematic research related implementations. A multi-Kinect system can be useful in exploiting spatial diversity to increase measurement accuracy. One of the main problems in deploying multi-Kinect systems is to estimate the pose, including the position and orientation of each Kinect. In this paper, a singular value decomposition (SVD) least-squares algorithm is extended to a more generic time-series based approach to solve this pose estimation problem utilising 3D positions of one or more joints in skeletons obtained from a multi-Kinect system. Additionally, computer simulations are performed to demonstrate the use and to evaluate the efficiency of the proposed algorithm. The former is further validated with a commercial Vicon system.

The installation of a Condition Monitoring System (CMS) on a mechanical machine (e.g., on a wind turbine) aims to reduce the operating costs by applying a predictive maintenance strategy. The CMS is composed of sensors acquiring signals from which system health indicators are computed and monitored. Part of those indicators are predefined depending on the monitored system kinematic and are computed by averaging large or narrow spectral bands. The averaging and the need for predefined thresholds for default detection may induce lots of false alarms while reducing the ability to detect the default early. To get precise health indicators reflecting each local meaningful spectral content, the AStrion software proposes a new data-driven monitoring strategy without any a priori on the measured signals. First, an automatic spectral analysis is applied to detect, characterize and classify the different spectral structures of the successive measured signals. These spectral structures can be either single spectral peaks, either peaks grouped in harmonic series or in modulation sidebands [1]. Second, these spectral structures are characterized by several features, including for example the number of peaks, the characteristic frequencies and the energy. This gives a snapshot of the system health at the signal acquisition time. To perform an automatic diagnosis of the system, the spectral evolution should be tracked along the time snapshots. In this paper, we propose a time tracking method based on McAulay & Quatieri algorithm [2] which has been designed originally for speech signals acquired on a continuous temporal basis. We have adapted [2] in order to account not only for single spectral peak evolution but also for the evolution of more complex structures such as harmonic series or modulation sidebands, even in the case of signals acquired on a non-regular temporal basis, as it is often the case. Moreover, an added sleep state makes the proposed method robust against nondetected spectral structures at a given time. Finally, the temporal evolution of the spectral structure features can be monitored and used as precise health indicators. The following figure is a result of the proposed method applied on real signals coming from a test bench designed in KAStrion project for simulating a wind turbine operation and for which the inner race of the main bearing has been damaged. Above, the time frequency map displays a zoom of the spectral peaks detected (around 20.000 per snapshot, represented by circles) and shows in blue the tracking from 44 to 189 operating hours of a spectral peak at 3.45 Hz. This particular peak evolves at 129 hours to become an harmonic series with more and more peaks and energy. Its energy evolution (plotted below) shows an increase which mirrors out a failure. In a following step [3], this spectral structure has been associated with the ball pass frequency of the inner ring of the main bearing. A dismantling of this bearing has confirmed the failure. This result shows the potential of the proposed data-driven method to create automatically relevant health indicators.

The mobile context of Mobile Wireless Sensor Networks (MWSN) limits the existence of a direct route from source to destination. A Disruption Tolerant Networking (DTN) architecture fits the requirements for such a context where messages need to be stored, carried and forwarded. For this kind of DTN applications, the goal is to achieve a high delivery ratio at low transmission cost with the lowest latency. Some DTN routing protocols use this ACK information to decrease the number of useless transmissions. Nevertheless in memory-constrained environments, the proportion of memory allocated to ACKs is a problem to study. This paper focuses on the use of acknowledgements (ACKs). We model the network with a Markov chain, to study the effect of ACKs on buffer time occupancy. Finally, an extensive set of simulations is run to analyse the influence of the memory proportion allocated to ACKs, on the performance.