Estimation of the underwater acoustic channel

This paper deals with the processing of real data recorded during the Oceanic Acoustic Tomography experiments « PASSTIME » carried out by the SHOM in October 2005. The transmitted signal waveform of interest is assumed to be known at the receiver side, and we address the problem of the channel impulse response estimation in a bayesian framework : the channel is modeled via a Bernoulli-Gaussian process, and estimation is achieved via a Gibbs sampler. As the processing of PASSTIME data emphasizes distorsions of the transmitted signal, we add a preliminary processing step for estimating the distorded waveform by means of a simple iterative procedure that alternatively estimates the channel impulse response via a Matching Pursuit approach, and the waveform itself by maximizing the likelihood. The proposed algorithms are applied to PASSTIME records, and prove to be very satisfactory.Dans cet article, nous nous intéressons au traitement des données réelles enregistrées au cours de la campagne de Tomographie Acoustique Océanique « PASSTIME » effectuée par le SHOM en Octobre 2005. Pour une forme d'onde transmise connue du récepteur, nous proposons de présenter le problème de l'estimation de la réponse impulsionnelle du canal acoustique sous-marin sous la forme d'un problème bayésien. Le canal est alors modélisé par un a priori Bernoulli-Gaussien, et l'estimateur est évalué à partir de simulations générées au moyen d'un algorithme de Gibbs. Comme le traitement des données PASSTIME met en évidence d'importantes distorsions subies par le signal, nous effectuons l'estimation préalable de la forme d'onde distordue au moyen d'un algorithme alternant l'estimation du canal par une méthode de type Matching Pursuit, et l'estimation de la forme d'onde au sens du maximum de vraisemblance. L'application aux données PASSTIME conduit à des résultats très satisfaisants

Best network chirplet-chain: Near-optimal coherent detection of unmodeled gravitation wave chirps with a network of detectors

The searches of impulsive gravitational waves (GW) in the data of the ground-based interferometers focus essentially on two types of waveforms: short unmodeled bursts and chirps from inspiralling compact binaries. There is room for other types of searches based on different models. Our objective is to fill this gap. More specifically, we are interested in GW chirps with an arbitrary phase/frequency vs. time evolution. These unmodeled GW chirps may be considered as the generic signature of orbiting/spinning sources. We expect quasi-periodic nature of the waveform to be preserved independent of the physics which governs the source motion. Several methods have been introduced to address the detection of unmodeled chirps using the data of a single detector. Those include the best chirplet chain (BCC) algorithm introduced by the authors. In the next years, several detectors will be in operation. The joint coherent analysis of GW by multiple detectors can improve the sight horizon, the estimation of the source location and the wave polarization angles. Here, we extend the BCC search to the multiple detector case. The method amounts to searching for salient paths in the combined time-frequency representation of two synthetic streams. The latter are time-series which combine the data from each detector linearly in such a way that all the GW signatures received are added constructively. We give a proof of principle for the full sky blind search in a simplified situation which shows that the joint estimation of the source sky location and chirp frequency is possible.Comment: 22 pages, revtex4, 6 figure

Multitarget likelihood for Track-Before-Detect applications with amplitude fluctuations

International audienceTrack-Before-Detect methods jointly detect and track one or several targets from raw sensor measurements. They often require the computation of the measurement likelihood conditionally to the hidden state that depends on the complex amplitudes of the targets. Since these amplitudes are unknown and fluctuate over time this likelihood must be marginalized over the complex amplitude (i.e. phase and modulus). It has been demonstrated in [1] that this marginalization can be done analytically over the phase in the monotarget case. In this article, we first propose to extend the marginalization to the modulus in a monotarget setting, and we show that closed-forms can be obtained for fluctuations of type Swerling 1 and 3. Second, we demonstrate that, in a multitarget setting, a closed-form can be obtained for the Swerling 1 case. For Swerling 0 and 3 models, we propose some approximation to alleviate the computation. Since many articles consider the case of squared modulus measurements, we also consider this specific case in mono and multitarget settings with Swerling 0, 1 and 3 fluctuations. Finally, we compare the performance in estimation and detection for the different cases studied and we show the gain, both in detection and estimation, of the complex measurement method over the squared modulus method, for any fluctuation model

Generalization and Improvement of the Levenshtein Lower Bound for Aperiodic Correlation

This paper deals with lower bounds on aperiodic correlation of sequences. It intends to solve two open questions. The first one is on the validity of the Levenshtein bound for a set of sequences other than binary sequences or those over the roots of unity. Although this result could be a priori extended to polyphase sequences, a formal demonstration is presented here, proving that it does actually hold for these sequences. The second open question is on the possibility to find a bound tighter than Welch’s, in the case of a set consisting of two sequences M = 2. By including the specific structure of correlation sequences, a tighter lower bound is introduced for this case. Besides, this method also provides in the cases M = 3 and M = 4 a tighter bound than the up-to-now tightest bound provided by Liu et al

A Joint Optimization for Coherent MIMO Radar

This paper deals with the optimization of a set of transmitted sequences and their associated mismatched filters, for the coherent MIMO radar. A minimization problem may be considered, with an objective function that measures for instance the correlations within the set. In the literature, it is usually solved alternatively, meaning that the optimization is performed on the sequences while the mismatched filters are set, and vice-versa. In this paper, an iterative method that proceeds jointly is introduced. Sequences are derived with a gradient descent, and mismatched filters are chosen in such a way that they are optimal in the ISL (Integrated Sidelobe Level) sense. Simulations show interesting results, as a joint optimization performs better than a separate one, even with relatively short sequences

Optimization Methods for Solving the Low Autocorrelation Sidelobes Problem

In this paper, a discussion is made on the optimization methods that can solve the low autocorrelation sidelobes problem for polyphase sequences. This paper starts with a description and a comparison of two algorithms that are commonly used in the literature: a stochastic method and a deterministic one (a gradient descent). Then, an alternative method based on the Random Walk Metropolis-Hastings algorithm is proposed, that takes the gradient as a search direction. It provides better results than a steepest descent alone. Finally, this autocorrelation question is handled differently, considering a mismatched filter. We will see that a mismatched filter performs impressively well on optimized sequences

Phase Code Optimization for Coherent MIMO Radar Via a Gradient Descent

In this paper, a gradient descent method is used to build radar waveform sequences with good autocorrelation and/or cross-correlation. The approach we propose is based on the energy, a function that measures the sidelobe level of a sequence, and its gradient. Then, we extend and apply it to the optimization of the coherent MIMO (Multiple Input Multiple Output) ambiguity function. We suggest to look for the transmitted signals that reduce the autocorrelation sidelobe level of the signal transmitted by the whole antenna. The obtained results, highlighted by the low sidelobe level of the ambiguity function, seem promising

On the furtivity of signals used in Oceanic Acoustic Tomography experiments

International audienceIn this paper, the furtivity of acoustic signals commonly used in oceanic acoustic tomography experiments is addressed. Three kinds of detectors are presented that can be used by an outsider to detect either unknown signals or signals showing the main characteristics of common tomographic signals. In the first case, the detector decision statistic is based on the signals energy, whereas in the second case it exploits either cyclostationarity properties of the signals or their time-frequency characteristics. Performance of these detectors on signals transmitted through a multipath underwater channel is studied

Estimation du canal acoustique sous-marin pour la tomographie acoustique par petits fonds

International audienc

Estimation de canaux multitrajets (application à la tomographie acoustique océanique active discrète)

Les canaux mutlitrajets apparaissent dans de nombreuses applications. Ainsi, en tomographie acoustique océanique active discrète, méthode d'observation de l'océan mettant en jeu l'émission d'ondes acoustiques peu détectables, les multitrajets proviennent de la réfraction de ces ondes par le milieu et de leurs réflexions sur la surface et le fond. L'objet de cette thèse est l'estimation de la réponse impulsionnelle du canal acoustique sous-marin à très faible SNR. En l'absence d'effets Doppler, le canal peut être modélisé par un vecteur creux. Dans un contexte bayésien, cette parcimonie peut être prise en compte par l'introduction d'un modèle Bernoulli-Gaussien. La maximisation de la vraisemblance a posteriori étant difficile, l'estimation s'effectue au moyen de méthodes de simulation de type MCMC. Les résultats obtenus, comparés aux bornes de Cramer-Rao, montrent en particulier l'importance de la prise en compte de la corrélation du bruit, notamment celle du filtrage adapté. En présence d'effets Doppler, nous proposons d'exploiter la structure particulière de la fonction d'ambiguïté des SBLM pour découpler l'estimation des paramètres. L'estimation des temps de retard s'effectue par application de la méthode sans Doppler aux sorties d'un banc de filtre décalées en fréquence. Les amplitudes et les décalages Doppler sont alors obtenus en chaque temps de retard estimé au moyen d'un algorithme de descente. Nous proposons de prendre en compte de possibles trajets simultanés en modélisant leur nombre au moyen d'une loi de Poisson. Par ailleurs, nous montrons qu'il est possible d'améliorer les résultats d'estimation à fort SNR en appliquant une stratégie d'annulation d'interférences. Enfin, nous proposons d'améliorer les performances de détection des trajets en utilisant l'information apportée par des enregistrements successifs. On remplace alors les variables de Bernoulli du modèle Bernoulli-Gaussin par un champ de Markov autologistique, qui permet de prendre en compte les similitudes entre positions de trajets voisins. Comme l'estimation, effectuée de nouveau au moyen d'une méthode MCMC, peut devenir coûteuse en temps de calcul, nous proposons d'accélérer la méthode en réduisant le nombre de sites visités au cours des simulations. Certains des algorithmes développés au cours de cette thèse ont finalement été testés sur des données réelles, ce qui a permis notamment de mettre en évidence la détection de trajets non résolus en sortie de filtrage adapté.RENNES1-BU Sciences Philo (352382102) / SudocBREST-Télécom Bretagne (290192306) / SudocSudocFranceF