A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks with Arbitrary Topology

We propose a new robust distributed linearly constrained beamformer which utilizes a set of linear equality constraints to reduce the cross power spectral density matrix to a block-diagonal form. The proposed beamformer has a convenient objective function for use in arbitrary distributed network topologies while having identical performance to a centralized implementation. Moreover, the new optimization problem is robust to relative acoustic transfer function (RATF) estimation errors and to target activity detection (TAD) errors. Two variants of the proposed beamformer are presented and evaluated in the context of multi-microphone speech enhancement in a wireless acoustic sensor network, and are compared with other state-of-the-art distributed beamformers in terms of communication costs and robustness to RATF estimation errors and TAD errors

On Convergence Analysis of Gradient Based Primal-Dual Method of Multipliers

© 2018 IEEE. Recently, the primal-dual method of multipliers (PDMM) has been proposed and successfully applied to solve a number of decomposable convex optimizations distributedly and iteratively. In this work, we study the gradient based PDMM (GPDMM), where the objective functions are approximated using the gradient information per iteration. It is shown that for a certain class of decomposable convex optimizations, synchronous GPDMM has a sublinear convergence rate of O(1/K) (where K denotes the iteration index). Experiments on a problem of distributed ridge regularized logistic regression demonstrate the efficiency of synchronous GPDMM

Approches tomographiques structurelles pour l'analyse du milieu urbain par tomographie SAR THR : TomoSAR

SAR tomography consists in exploiting multiple images from the same area acquired from a slightly different angle to retrieve the 3-D distribution of the complex reflectivity on the ground. As the transmitted waves are coherent, the desired spatial information (along with the vertical axis) is coded in the phase of the pixels. Many methods have been proposed to retrieve this information in the past years. However, the natural redundancies of the scene are generally not exploited to improve the tomographic estimation step. This Ph.D. presents new approaches to regularize the estimated reflectivity density obtained through SAR tomography by exploiting the urban geometrical structures.La tomographie SAR exploite plusieurs acquisitions d'une même zone acquises d'un point de vue légerement différent pour reconstruire la densité complexe de réflectivité au sol. Cette technique d'imagerie s'appuyant sur l'émission et la réception d'ondes électromagnétiques cohérentes, les données analysées sont complexes et l'information spatiale manquante (selon la verticale) est codée dans la phase. De nombreuse méthodes ont pu être proposées pour retrouver cette information. L'utilisation des redondances naturelles à certains milieux n'est toutefois généralement pas exploitée pour améliorer l'estimation tomographique. Cette thèse propose d'utiliser l'information structurelle propre aux structures urbaines pour régulariser les densités de réflecteurs obtenues par cette technique

Wideband Direction of Arrival estimation and sparse modeling for underwater surveillance

In underwater surveillance sources, such as ships or submarines, are localized using the acoustic noise emitted by the source engines, propellers and other machinery. The acoustic signals propagate in the sea and are recorded with an array of acoustic sensors. Processing the recorded signals to obtain the locations of the sources is known as Direction of Arrival (DOA) estimation in the field of signal processing. A simple mathematical model relating the sensor array geometry to the DOA of the source exists when the frequency of the source signal is known. The model is directly applicable to a narrowband DOA estimation problem where the energy of the source signals is concentrated around a single carrier frequency. For underwater surveillance, however, the source signals are wideband which complicates the problem. This thesis reviews existing methods for wideband DOA estimation: Simple extensions of well known narrowband methods MVDR and MUSIC, the so called coherent methods and the most recent methods belonging into the sparse framework. An original idea for extending MVDR using a likelihood based combining of subbands, MVDR-LBC is developed. The thesis models the sensor signals as a sparse autoregressive process by linear prediction and the original algorithm GRLS. The sparse model is shown to be effective compared to the conventional non-sparse one. The model can be used to compress the data recorded in underwater surveillance. The wideband DOA estimation methods are tested with a number of simulations and with real data recorded in the sea. MVDR is shown to be robust and effective, the accuracy and resolution of which can be improved using MVDR-LBC. MUSIC provides good resolution, is computationally efficient and can be implemented quite simply. The coherent methods are the most complicated and need good pre-estimations for the source directions but can resolve close sources best