Automatic Detection of the Number of Raypaths in a Shallow-Water Waveguide

International audienceCorrect identification and tracking of stable raypaths are critical for shallow-water acoustic tomography. Separating raypaths using high-resolution methods has been presented to improve resolution ability based on the prior knowledge of the number of raypaths. It is clear that the precise knowledge of the number of raypaths largely determines the separation performance. Therefore, a noise-whitening exponential fitting test (NWEFT) using short-length samples is proposed in this paper to automatically detect the number of raypaths in a shallow-water waveguide. Two information-theoretic criteria are considered as comparative methods in terms of the capability of correct detection. Their performances are tested with simulation data and real data obtained from a small-scale experiment. The experimental results show that the NWEFT can provide satisfactory detection compared to the two classic information-theoretic criteria--the Akaike information criterion (AIC) and the minimum description length (MDL). MDL is asymptotically consistent while AIC overestimates even if analyzed asymptotically. Compared to these criteria, the proposed method is more suitable for short-length data

Decomposition and dictionary learning for 3D trajectories

International audienceA new model for describing a three-dimensional (3D) trajectory is proposed in this paper. The studied trajectory is viewed as a linear combination of rotatable 3D patterns. The resulting model is thus 3D rotation invariant (3DRI). Moreover, the temporal patterns are considered as shift-invariant. This paper is divided into two parts based on this model. On the one hand, the 3DRI decomposition estimates the active patterns, their coefficients, their rotations and their shift parameters. Based on sparse approximation, this is carried out by two non-convex optimizations: 3DRI matching pursuit (3DRI-MP) and 3DRI orthogonal matching pursuit (3DRI-OMP). On the other hand, a 3DRI learning method learns the characteristic patterns of a database through a 3DRI dictionary learning algorithm (3DRI-DLA). The proposed algorithms are first applied to simulation data to evaluate their performances and to compare them to other algorithms. Then, they are applied to real motion data of cued speech, to learn the 3D trajectory patterns characteristic of this gestural language

Marine mammal's directivity in geoacoustic inversion scheme

International audienceGervaise & al 2011 and Barazzutti & al 2013 described the general structure of a scheme to estimate the nature of superficial sediment in shallow waters using marine mammal's whistles and a single receiver. The multipath structure of calls given by a spectrogram is used to estimate the source characteristics and the superficial sea bottom features. A field application of this method was presented in [11] using controlled signals similar to marine mammal's vocalizations in a shallow water environment on a sandy bottom. However, contrary to the source used during that experiment, marine mammals are directive sources and the directivity loss underwent by the multipath must be taken into account in our inversion process. Indeed, the directivity is a function of frequency and emission angle (sound-source azimuth), and impacts each path differently. Thus the bottom path, once corrected from transmission loss, must be corrected from directivity loss before being used to estimate the bottom features. The emission angle can easily be geometrically related to the arrival angle and a specific unknown angle we called attitude (source orientation in space during the emission). However, the directivity patterns of marine mammals are not well studied yet, especially for vocalizations (e.g. directivity model assumption - Au 1993[4], directivity pattern measurement - Au & al 2012[9], etc.) and contrary to other mammals the unknown "attitude" parameter is not that easy to observe (e.g. Dantzker & al 1999). Our communication aims at describing different methods to estimate the "attitude" angle and the directivity loss for marine mammals. Their performances and limits are evaluated using simulated data

Utilisation de warping temporel pour étendre le domaine de fonctionnement d’un procédé d’inversion géoacoustique passive

National audienceThis paper presents new tools to improve a geoacoustic inversion scheme relying on the inversion of marine mammals vocalizations recorded on a single hydrophone. This method makes best use of the multipath propagation and time-frequency signature of vocal calls. The classical signal processing tools based on spectrogram limit both the range of the scheme and the signals panel that can be processed. In this paper, we introduce new tools based on temporal warping that allow high resolution paths separation. Each echo has to be transformed into a pure frequency. Therefore, they are warped according to the frequency law of the signal estimated according to the first echo. The warping tools enable a high resolution of each echo in the time-frequency warped domain. Applying the warping on a moving window, one can estimate the impulsive response (IR) of the channel and deduce the source location, grazing angle and transmission losses. Then, as warping operators conserve energy, the path levels can be estimated using the time-frequency warped representation. Through the processing of several signals, we get a curve of an estimation of the reflection coefficient that feeds the inversion algorithm. The theory of this extended method is described and its performances are evaluated on a controlled real data set in the Gulf of Lion. The range and the recording duration have been improved (range from 300 to 900 meters and duration multiplied by 2.5). With the new scheme, we have access to a set of geoacoustic parameters allowing to better describe the bottom features.Cet article introduit de nouveaux outils pour le traitement de signal dans le cadre d’un schéma d’inversion géoacoustique passive s’appuyant sur des modulations de fréquence de mammifères marins captés sur un unique hydrophone. La méthode se base sur l’utilisation de la propagation multitrajet pour extraire l’information nécessaire à l’estimation du coefficient de réflexion du sédiment superficiel. Jusqu’à présent, l’utilisation d’outils de traitement du signal basés sur le spectrogramme limitait la portée et les signaux candidats à l’inversion, ceux-ci devant présenter des échos résolus dans le plan temps-fréquence au regard des outils mis en œuvre. Ce papier propose l’ajout d’une nouvelle étape de traitement basée sur des opérateurs de déformation temporelle (warping) permettant la séparation haute résolution des échos. Les échos sont transformés en composantes de fréquences pures par application d’un opérateur de déformation avec pour loi de déformation, la loi de fréquence instantanée du signal. En contexte passif, cette loi est estimée à partir du premier écho reçu. L’application de l’opérateur de déformation sur une fenêtre temporelle glissante permet d’obtenir la réponse impulsionnelle (RI) du canal. Celle-ci est ensuite utilisée pour estimer la position de la source, l’angle de rasance et les pertes de transmission. Le warping conservant l’énergie, le niveau de chaque écho peut être alors estimé sur le signal déformé sans devoir revenir dans l’espace temporel initial. A l’instar de la méthode d’inversion de base, le traitement d’une série d’émissions fournit une courbe estimée du coefficient de réflexion en fonction des angles de rasance. Cette courbe sert d’observable à l’algorithme d’inversion. Le résultat important de cette étude est que ces nouveaux outils permettent d’augmenter la portée et la diversité des signaux candidats à la méthode d’inversion. Les performances et la validité du schéma d’inversion étendu ont été évaluées sur données synthétiques puis confrontées avec succès aux émissions réelles dans le golfe du Lion sur lesquelles la méthode d’origine avait été précédemment évaluée. La portée a été triplée et la durée d’enregistrement utile multipliée par un facteur 2,5 grâce aux nouveaux outils. Avec ces nouveaux outils, nous avons accès à un ensemble de paramètres géoacoustiques permettant de mieux décrire les caractéristiques du fond marin

Quaternionic Sparse Approximation

ISBN 978-0-8176-4267-9International audienceIn this paper, we introduce a new processing procedure for quaternionic signals through consideration of the well-known orthogonal matching pursuit (OMP), which provides sparse approximation. We present a quaternionic extension, the quaternionic OMP, that can be used to process a right-multiplication linear combination of quaternionic signals. As validation, this quaternionic OMP is applied to simulated data. Deconvolution is carried out and presented here with a new spikegram that is designed for visualization of quaternionic coefficients, and finally this is compared to multivariate OMP

About QLMS Derivations

International audienceIn this letter, a review of the quaternionic least mean squares (QLMS) algorithm is proposed. Three versions coming from three derivation ways exist: the original QLMS based on component wise gradients, the HR-QLMS based on a quaternion gradient operator and iQLMS based on an involutions-gradient. Noting and investigating the differences between the three QLMS formulations, we show that the original QLMS suffers from a mistake in the derivation calculus. Thus, we propose to derive rigorously the criterion following the first way, giving the correct version of QLMS. A comparison with the other QLMS versions validates these results on simulated data

Color Sparse Representations for Image Processing: Review, Models, and Prospects

International audienceSparse representations have been extended to deal with color images composed of three channels. A review of dictionary-learning-based sparse representations for color images is made here, detailing the differences between the models, and comparing their results on real data and simulated data. These models are considered in a unifying framework that is based on the degrees of freedom of the linear filtering/transformation of the color channels. Moreover, this allows it to be shown that the scalar quaternionic linear model is equivalent to constrained matrix-based color filtering, which highlights the filtering implicitly applied through this model. Based on this reformulation, the new color filtering model is introduced, using unconstrained filters. In this model, spatial morphologies of color images are encoded by atoms, and colors are encoded by color filters. Color variability is no longer captured in increasing the dictionary size, but with color filters, this gives an efficient color representation

Detection of deterministic transient signals in white Gaussian noise by statistical analysis of similarity matrix coefficients

International audienceOver the past decades, recurrence plot analysis has become a popular tool for analyzing dynamical systems. As recurrence plots show different patterns that depend on the state of the system (random, deterministic, chaotic), several approaches have been proposed in the literature to quantify and distinguish between these different states. Most of existing methods rely on metrics called recurrence quantification analysis (RQA), to decide whether the time series is random or deterministic. In this presentation, we propose a new detection scheme that only relies on the analysis of the statistical distribution of the similarity matrix coefficients, to decide whether the measured signal is a white Gaussian noise or a deterministic transient. Our hypothesis is that if the measured time series is a white Gaussian noise, then the similarity matrix coefficients will follow a certain distribution, whereas if the measured time series contains a deterministic transient, the similarity matrix coefficients will follow another distribution.First, we make some analytical development to derive the mathematical expressions of the expected distribution for the similarity matrix coefficients, when the input signal is a white Gaussian noise. Then, we compare this analytic distribution with the empirical distribution obtained for a given unknown measured signal. This comparison is equivalent to a goodness-of-fit test and is achieved using divergence measures. Finally, the value of this divergence measure is compared to a detection threshold in order to decide whether the analytical and empirical distributions look alike or not, and so if the measured signal is a noise only or deterministic.The performances of the proposed detector are assessed by use of receiver operating characteristic (ROC) curves. Deterministic signals to be detected are pure cosine and impulses. Influences of parameters like the embedding m, the similarity measure and the divergence measure on the performances are discussed. Finally, the proposed detector is compared with that of the energy detector and the matched filter detector, which are commonly used is signal processing. Results of this performance analysis shows that the proposed detector outperforms the energy detector, giving a probability of detection 10% to 50% higher, and has a similar performance to that of a matched-filter detector

Time-Difference-of-Arrival Estimation Based on Cross Recurrence Plots, with Application to Underwater Acoustic Signals

International audienceThe estimation of the time difference of arrival (TDOA) consists of the determination of the travel-time of a wavefront between two spatially separated receivers , and it is the first step of processing systems dedicated to the identification, localization and tracking of radiating sources. This article presents a TDOA estima-tor based on cross recurrence plots and on recurrence quantification analysis. Six recurrence quantification analyses measures are considered for this purpose, including two new ones that we propose in this article. Simulated signals are used to study the influence of the parameters of the cross recurrence plot, such as the embedding dimension, the similarity function, and the recurrence threshold, on the reliability and effectiveness of the estimator. Finally, the proposed method is validated on real underwater acoustic data, for which the cross recurrence plot estimates correctly 77.6% of the TDOAs, whereas the classical cross-correlation estimates correctly only 70.2% of the TDOAs

Similarity matrix analysis and divergence measures for statistical detection of unknown deterministic signals hidden in additive noise

International audienceThis Letter proposes an algorithm to detect an unknown deterministic signal hidden in additive white Gaussian noise. The detector is based on recurrence analysis. It compares the distribution of the similarity matrix coefficients of the measured signal with an analytic expression of the distribution expected in the noise-only case. This comparison is achieved using divergence measures. Performance analysis based on the receiver operating characteristics shows that the proposed detector outperforms the energy detector, giving a probability of detection 10% to 50% higher, and has a similar performance to that of a sub-optimal filter detector