Cette thèse est consacrée à l'étude de la localisation d'objets enfouis dans acoustiques sous-marins en utilisant les méthodes de traitement d'antenne et les ondes acoustiques. Nous avons proposé un modèle bien adapté en tenant compte le phénomène physique au niveau de l'interface eau/sédiment. La modélisation de la propagation combine donc la contribution de l'onde réfléchie et celle de l'onde réfractée pour déterminer un nouveau vecteur directionnel. Le vecteur directionnel élaboré à partir des modèles de diffusion acoustique est utilisé dans la méthode MUSIC au lieu d'utiliser le modèle d'onde plane habituel. Cette approche permet d'estimer à la fois coordonnées d'objets (angle et distance objet-capteur) de forme connue, quel que soit leur emplacement vis à vis de l'antenne, en champ proche ou en champ lointain. Nous remplaçons l'étape de décomposition en éléments propres par des algorithmes plus rapides. Nous développons un algorithme d'optimisation plus élaboré consiste à combiner l'algorithme DIRECT (DIviding RECTangles) avec une interpolation de type Spline, ceci permet de faire face au cas d'antennes distordues à grand nombre de capteurs, tout en conservant un temps de calcul faible. Les signaux reçus sont des signaux issus de ce même capteur, réfléchis et réfractés par les objets et sont donc forcément corrélés. Pour cela, nous d'abord utilisons un opérateur bilinéaire. Puis nous proposons une méthode pour le cas de groupes indépendants de signaux corrélés en utilisant les cumulants. Ensuit nous présentons une méthode en utilisant la matrice tranche cumulants pour éliminer du bruit Gaussien. Mais dans la pratique, le bruit n'est pas toujours gaussien ou ses caractéristiques ne sont pas toujours connues. Nous développons deux méthodes itératives pour estimer la matrice interspectrale du bruit. Le premier algorithme est basé sur une technique d'optimisation permettant d'extraire itérativement la matrice interspectrale du bruit de la matrice interspectrale des observations. Le deuxième algorithme utilise la technique du maximum de vraisemblance pour estimer conjointement les paramètres du signal et du bruit. Enfin nous testons les algorithmes proposés avec des données expérimentales et les performances des résultats sont très bonnes. / This thesis is devoted to the study of the localization of objects buried in underwater acoustic using array processing methods and acoustic waves. We have proposed a appropriate model, taking into account the water/sediment interface. The propagation modeling thus combines the reflected wave and the refracted wave to determine a new directional vector. The directional vector developed by acoustic scattering model is used in the MUSIC method instead of the classical plane wave model. This approach can estimate both of the object coordinates (angle and distance sensor-object) of known form, in near field or far field. We propose some fast algorithms without eigendecompostion. We combine DIRECT algorithm with spline interpolation to cope with the distorted antennas of many sensors, while maintaining a low computation time. To decorrelate the received signals, we firstly use a bilinear operator. We propose a method for the case of independent groups of correlated signals using the cumulants. Then we present a method using the cumulants matrix to eliminate Gaussian noise. But in practice, the noise is not always Gaussian or the characteristics are not always known. We develope two iterative methods to estimate the interspectral matrix of noise. The first algorithm is based on an optimization technique to extract iteratively the interspectral matrix of noise. The second algorithm uses the technique of maximum likelihood to estimate the signal parameters and the noise. Finally we test the proposed algorithms with experimental data. The results quality is very good.

Data acquisition from multiple analog channels is an important function in many embedded devices used in avionics, medical electronics, robotics and space applications. It is desirable to engineer these systems to reduce their size, power consumption, heat dissipation and cost. The goal of this research is to explore designs that exploit a priori knowledge of the input signals in order to achieve these objectives. Sparsity is a commonly observed property in signals that facilitates sub-Nyquist sampling and reconstruction through compressed sensing, thereby reducing the number of A to D conversions. New architectures are proposed for the real-time, compressed acquisition of streaming signals. A. It is demonstrated that by sampling a collection of signals in a multiplexed fashion, it is possible to efficiently utilize all the available sampling cycles of the analogue-to-digital converters (ADCs), facilitating the acquisition of multiple signals using fewer ADCs. The proposed method is modified to accommodate more general signals, for which spectral leakage, due to the occurrence of non-integral number of cycles in the reconstruction window, violates the sparsity assumption. When the objective is to only detect the constituent frequencies in the signals, as against exact reconstruction, it can be achieved surprisingly well even in the presence of severe noise (SNR ~ 5 dB) and considerable undersampling. This has been applied to the detection of the carrier frequency in a noisy FM signal. Information redundancy due to inter-signal correlation gives scope for compressed acquisition of a set of signals that may not be individually sparse. A scheme has been proposed in which the correlation structure in a set of signals is progressively learnt within a small fraction of the duration of acquisition, because of which only a few ADCs are adequate for capturing the signals. Signals from the different channels of EEG possess significant correlation. Employing signals taken from the Physionet database, the correlation structure of nearby EEG electrodes was captured. Subsequent to this training phase, the learnt KLT matrix has been used to reconstruct signals of all the electrodes with reasonably good accuracy from the recordings of a subset of electrodes. Average error is below 10% between the original and reconstructed signals with respect to the power in delta, theta and alpha bands: and below 15% in the beta band. It was also possible to reconstruct all the channels in the 10-10 system of electrode placement with an average error less than 8% using recordings on the sparser 10-20 system. In another design, a set of signals are collectively sampled on a finer sampling grid using ADCs driven by phase-shifted clocks. Thus, each signal is sampled at an effective rate that is a multiple of the ADC sampling rate. So, it is possible to have a less steep transition between the pass band and the stop band, thereby reducing the order of the anti-aliasing filter from 30 to 8. This scheme has been applied to the acquisition of voltages proportional to the deflection of the control surfaces in an aerospace vehicle. The idle sampling cycles of an ADC that performs compressive sub-sampling of a sparse signal, can be used to acquire the residue left after a coarse low-resolution sample is taken in the preceding cycle, like in a pipelined ADC. Using a general purpose, low resolution ADC, a DAC and a summer, one can acquire a sparse signal with double the resolution of the ADC, without having to use a dedicated pipelined ADC. It has also been demonstrated as to how this idea can be applied to achieve a higher dynamic range in the acquisition of fetal electrocardiogram signals. Finally, it is possible to combine more than one of the proposed schemes, to handle acquisition of diverse signals with di_erent kinds of sparsity. The implementation of the proposed schemes in such an integrated design can share common hardware components so as to achieve a compact design.