Algorithms based on sparsity hypotheses for robust estimation of the noise standard deviation in presence of signals with unknown distributions and concurrences

Inmany applications, d-dimensional observations result fromthe randompresenceor absence of randomsignals in independent and additivewhite Gaussiannoise. An estimate of the noise standard deviation can then be very useful todetect or to estimate these signals, especially when standard likelihood theory cannot apply because of too little prior knowledge about the signal probability distributions. Recent results and algorithms have then emphasized the interest of sparsity hypotheses to design robust estimators of the noise standard deviation when signals have unknown distributions. As a continuation, the present paper introduces a new robust estimator for signals with probabilities of presence less than or equal to one half. In contrast to the standard MAD estimator, it applies whatever the value of d. This algorithm is applied to image denoising by wavelet shrinkage as well as to non-cooperative detection of radiocommunications.In both cases, the estimator proposed in the present paper outperforms the standard solutions used in such applications and based on the MAD estimator. The Matlab code corresponding to the proposed estimator is available at http://perso.telecom-bretagne.eu/pasto

A Parametric Replay-Based Framework for Underwater Acoustic Communication Channel Simulation

International audienceThis paper lays the foundation of an underwater acoustic channel simulation methodology that is halfway between parametric modeling and stochastic replay of at-sea measurements of channel impulse responses. The motivation behind this approach is to extend the scope of use of replay-based methods by allowing some parameterization of the channel properties while complying with some level of realism. Based on a relative entropy minimization between the original channel impulse response and the simulated one, the idea is to deliberately distort the original channel statistics in order to meet some specified constraints

Achievable Rates of Underwater Acoustic OFDM Systems over Highly Dispersive Channels

International audienceUnlike the capacity of other channels, the capacity of the shallow water UAC channel has been seldomly addressed. Motivated by recent results in information theory, this paper investigates achievable rates of underwater acoustic OFDM systems. We consider channels where time and frequency dispersion is high enough that (i) neither the transmitter nor the receiver can have a priori knowledge of the channel state information, and (ii) intersymbol/intercarrier interference (ISI/ICI) cannot be neglected in the information theoretic treatment

Blind noise variance estimation for OFDMA signals

International audienceWe present two new noise variance estimation methods for OFDMA signals transmitted through an unknown multipath fading channel. We focus on blind estimation as it does not require any pilot sequences and is therefore applicable to contexts,such as cognitive radio for instance, where little prior signal knowledge is available. The two estimators are respectively based on the time-frequency sparsity of OFDMA signals and on the redundancy induced by the cyclic prefix. Numerical simulations compare the performance of the two algorithms and highlight their complementarity

Cyclostationarity of Communication Signals in Underwater Acoustic Channels

International audienceThe effect of underwater acoustic propagation on the cyclostationary features of communication signals is modeled and analyzed. Two kinds of channels are considered: the multiscale-multilag channel, over which mobile and wideband acoustic systems usually communicate, and the dispersive channel resulting from low-frequency modal propagation in shallow water. It is shown that multiscale-multilag channels transform cyclostationary signals into a sum of velocity and acceleration-dependent time-warped cyclostationary processes. This time-warping is carefully taken into account to efficiently recover the cyclostationary features. On the other hand, it is found that low-frequency dispersive channels preserve the original periodicity but attenuate the shorter cycles and spread the correlations. To illustrate the theoretical results, applications with simulated and real data are also presented. Specifically, the problem of estimating time-varying Doppler scales is addressed for multiscale-multilag channels as well as the detection of signals with unique cyclostationary signatures. The example of blind symbol-rate estimation applied to covert communications in dispersive channels is also discussed. Special attention is paid to PSK, QAM, OFDM and DSSS signals. Accompanying supplementary material provides the MATLAB code used for the estimation and detection examples

A Multifamily GLRT for CFAR Detection of Signals in a Union of Subspaces

International audienceWe consider the problem of detecting an unknown signal that lies in a union of subspaces (UoS) and that is observed in additive white Gaussian noise with unknown variance. The main contribution of this paper is the derivation of a detector that can accommodate a union made of nested subspaces. This detector includes the generalized likelihood ratio test (GLRT) as a special case when the subspace dimensions are all identical. It relies on the framework of multifamily likelihood ratio tests (MFLRT) and is shown by numerical examples to achieve better performance than existing detectors

Joint signal detection and channel estimation in multi-scale multi-lag underwater acoustic environments

International audienceWe consider the problem of jointly detecting a known signal and estimating the channel in the frequency range used by underwater acoustic communication systems. A multi-scale multi-lag propagation environment is considered and no prior knowledge of the channel order is assumed. The proposed detection/estimation method is based on the framework of multifamily generalized likelihood ratio tests applied to a signal lying in a union of subspaces. The result is a "tuning-free" orthogonal matching pursuit algorithm with a stopping criterion that does not require the knowledge of the number of channel taps or the noise variance. The performance is illustrated with replay simulations using real shallow-water channels measured in the Mediterranean Sea. Numerical results show that the proposed method outperforms competing algorithms in terms of both detection probability and channel estimation error. In addition, channel estimation does not exhibit a performance floor as observed with fixed-order-based approaches