On considère un réseau de radars MIMO dont on cherche à déterminer la meilleure répartition de largeurs de bande et de puissance entre les différentes antennes émettrices en vue d’obtenir une certaine précision de la localisation d’une cible unique. Plus précisément, on s’intéresse ici à l’allocation optimale de bande seule, ainsi qu’à l’allocation optimale conjointement de bande et de puissance. Cette allocation s’effectue par la minimisation de la borne de Cramér-Rao. Le problème d’optimisation non-convexe obtenu est résolu par un algorithme de programmation par différence de fonctions convexes. Les résultats numériques montrent que l’allocation conjointe fournit les meilleures performances, et que d’autre part l’allocation de bande joue un rôle prépondérant dans ces performances. De plus, une borne inférieure sur la borne de Cramér-Rao optimale théorique, difficilement calculable, a également été définie, qui montre la qualité de la solution quasi-optimale.

This paper presents a demodulation algorithm for automatic identification system (AIS) signals received by a satellite. The main contribution of this work is to consider the phase recovery problem for an unknown modulation index, coupled with a time-varying phase shift. The proposed method is based on a demodulator introduced in a previous paper based on a Viterbi-type algorithm applied to an extended trellis. The states of this extended trellis are composed of a trellis-code state and of a cyclic redundancy check state. The bit stuffing mechanism is taken into account by defining specific conditional transitions in the extended trellis. This algorithm estimates and tracks the phase shift by modifying the Euclidean distance used in the trellis. Simulation results obtained with and without phase tracking are presented and compared in the context of the AIS system.

This work addresses the problem of reconstruction of spectra F(ω) from the irregular sampling of autocorrelation functions f(t). We assume that F(ω) has bounded support [-π, π] : The number 2L of sampling times is such that the variation of F(ω) on intervals of length π/L is small enough to lead to a good interpolation of this function. Reconstruction formulas are based on PNS (Periodic Nonuniform Sampling) plans. They allow reconstructions not demanding periodic resampling and suppress two stages in classical computations. The method can also be easily generalized to spectra in symmetric frequency bands (bandpass signals).

The concept of delay/Doppler radar altimeter has been under study since the mid 90’s, aiming at reducing the measurement noise (by increasing the number of observations) and increasing the along-track resolution (by using the information of the Doppler frequency) in comparison with the conventional pulse limited altimeters. This paper introduces a semi-analytical model for delay/Doppler altimetry. The performance of the proposed model and the resulting estimation strategy are evaluated via simulations conducted on synthetic and real data. A comparison between conventional altimetry and delay/Doppler altimetry provides very promising results in favor of the proposed model.

This paper proposes a method for selective digital-to-analog conversion of a stationary band-pass random process submitted to interference. This method simultaneously performs the signal digital-to-analog conversion and the interference rejection from the observed process samples: it does not require any preliminary demodulation of the bandpass observed process and the filtering is performed in the time domain using an explicit expression of the filter taps. The method uses a particular non uniform sampling scheme called Periodic Non-uniform Sampling of order 2 (PNS2). The PNS2 sampling scheme uses two interleaved sample sequences. Appropriate formulas are derived to reconstruct the signal from its non-uniform samples while removing the interference by a selective filtering. The reconstruction from an infinite observation window (an infinite number of samples) is exact. However, in practical applications, the digital-to-analog conversion is generally performed in real time using a finite sliding observation window (a finite number of samples). Thus the reconstruction formulas should also demonstrate a high convergence rate. This paper proposes reconstruction formulas with increasing convergence rates using filters with increasingly regular transfer functions. The proposed method has been tested through simulations according to experimental parameters.

In a wide variety of applications, multifractal analysis has been abundantly used for the characterization and interpretation of scale invariance properties of experimental data. The parameter of multifractality or intermittency plays a central role in such an analysis as it enables the discrimination of large classes of scale invariance model processes. Yet, its estimation remains delicate in situations where sample size is limited since classical estimators, relying on simple linear regressions, exhibit large bias and / or variability, which severely limits the practical use of such estimates. To overcome such limitations, this contribution proposes an alternative estimation procedure. Its originality is twofold : First, we construct a generic semi-parametric model for the statistics of the logarithm of wavelet leaders (a declination, adapted for multifractal analysis, of classically used wavelet coefficients) for multiplicative cascade based multifractal processes. Second, we develop a Bayesian formulation of the estimation problem as well as an MCMC algorithm for approximating the associated posterior distributions and Bayesian estimators. The relevance of the proposed semi-parametric model and the performance of the Bayesian estimators are assessed by means of numerical simulations. We show that the proposed Bayesian procedure yields significantly improved estimation performance.

There has been a renewed interest at the Internet Engineering Task Force (IETF) in using Less-than-Best Effort (LBE) methods for background applications. IETF recently published a RFC for Low Extra Delay Background Transport (LEDBAT), a congestion control algorithm for LBE transmissions. This paper provides an analysis of LEDBAT performance over congested large bandwidth X delay product (LBDP) networks, and assesses the validity of having a fixed target queuing time. In particular, we lead a study of the impact of this target queuing delay when LEDBAT is used over 4G satellite networks. The rationale is to explore the possibility to grab the unused 4G satellite links' capacity to carry non-commercial traffic. We show that this is achievable with LEDBAT. However, depending on the fluctuation of the load, performance improvements could be obtained by properly setting the target value. We generalize this evaluation over different congested LBDP networks and confirm that the target value might need to be adjusted to networks' and traffic's characteristics. Further work will study whether and how this parameter should be dynamically adapted, and LEDBAT's congestion control improved.

Both WSNs (Wireless Sensor Networks) and observation satellites are able to get measurements from a geographic area. To interconnect these technologies, we propose to use a store-carry-and-forward architecture relying on the DTN (Disruption and Delay Tolerant Networking) Bundle Protocol. This architecture aims at being generic, so it is application-agnostic and suits a wide range of scenarios. WSN may collect sporadically large data volume while terrestrial stations communicating with Low Earth Orbit (LEO) satellites have to endure long link disruptions when the satellite is not in the line of sight. These sporadic growths within the WSN coupled with the large latency on satellite links require to schedule data to provide quality of service to several flows. We propose a scheduling policy based on deadline of Bundles and compare it with classical DTN solutions.

This paper focuses on the performance of time of arrival estimators for distress beacon signals which are defined by pulses with smooth transitions. These signals are used in the satellite-based search and rescue Cospas-Sarsat system. We propose a signal model based on sigmoidal functions. Closed-form expressions for the modified Cramér-Rao bounds associated with the parameters of this model are derived. The obtained expressions are easy to interpret since they analytically depend on the system parameters. Simulations conducted on realistic search and rescue signals show good agreement with the theoretical results.

We consider the adaptive beamforming or adaptive detection problem in the case of signal contaminated training samples, i.e., when the latter may contain a signal-like component. Since this results in a significant degradation of the signal to interference and noise ratio at the output of the adaptive filter, we investigate a scheme to jointly detect the contaminated samples and subsequently take this information into account for estimation of the disturbance covariance matrix. Towards this end, a Bayesian model is proposed, parameterized by binary variables indicating the presence/absence of signal-like components in the training samples. These variables, together with the signal amplitudes and the disturbance covariance matrix are jointly estimated using a minimum mean-square error (MMSE) approach. Two strategies are proposed to implement the MMSE estimator. First, a stochastic Markov Chain Monte Carlo method is presented based on Gibbs sampling. Then a computationally more efficient scheme based on variational Bayesian analysis is proposed. Numerical simulations attest to the improvement achieved by this method compared to conventional methods such as diagonal loading. A successful application to real radar data is also presented.