Gain and phase calibration of sensor arrays from ambient noise by cross-spectral measurements fitting

We address the problem of blind gain and phase calibration of a sensor array from ambient noise. The key motivation is to ease the calibration process by avoiding a complex procedure setup. We show that computing the sample covariance matrix in a diffuse field is sufficient to recover the complex gains. To do so, we formulate a non-convex least-square problem based on sample and model covariances. We propose to obtain a solution by low-rank matrix approximation, and two efficient proximal algorithms are derived accordingly. The first one solves the problem modified with a convex relaxation to guarantee that the solution is a global minimizer, and the second one directly solves the initial non-convex problem. We investigate the efficiency of the proposed algorithms by both numerical and experimental results according to different sensing configurations. These show that efficient calibration highly depends on how the measurements are correlated. That is, estimation is achieved more accurately when the field is spatially over-sampled.Comment: submitted to the Journal of the Acoustical Society of Americ

A Universal Framework for Holographic MIMO Sensing

This paper addresses the sensing space identification of arbitrarily shaped continuous antennas. In the context of holographic multiple-input multiple-output (MIMO), a.k.a. large intelligent surfaces, these antennas offer benefits such as super-directivity and near-field operability. The sensing space reveals two key aspects: (a) its dimension specifies the maximally achievable spatial degrees of freedom (DoFs), and (b) the finite basis spanning this space accurately describes the sampled field. Earlier studies focus on specific geometries, bringing forth the need for extendable analysis to real-world conformal antennas. Thus, we introduce a universal framework to determine the antenna sensing space, regardless of its shape. The findings underscore both spatial and spectral concentration of sampled fields to define a generic eigenvalue problem of Slepian concentration. Results show that this approach precisely estimates the DoFs of well-known geometries, and verify its flexible extension to conformal antennas

Design and implementation of a multi-octave-band audio camera for realtime diagnosis

Noise pollution investigation takes advantage of two common methods of diagnosis: measurement using a Sound Level Meter and acoustical imaging. The former enables a detailed analysis of the surrounding noise spectrum whereas the latter is rather used for source localization. Both approaches complete each other, and merging them into a unique system, working in realtime, would offer new possibilities of dynamic diagnosis. This paper describes the design of a complete system for this purpose: imaging in realtime the acoustic field at different octave bands, with a convenient device. The acoustic field is sampled in time and space using an array of MEMS microphones. This recent technology enables a compact and fully digital design of the system. However, performing realtime imaging with resource-intensive algorithm on a large amount of measured data confronts with a technical challenge. This is overcome by executing the whole process on a Graphic Processing Unit, which has recently become an attractive device for parallel computing

Walsh Meets OAM in Holographic MIMO

Holographic multiple-input multiple-output (MIMO) is deemed as a promising technique beyond massive MIMO, unleashing near-field communications, localization, and sensing in the next-generation wireless networks. Semi-continuous surface with densely packed elements brings new opportunities for increased spatial degrees of freedom (DoFs) and spectrum efficiency (SE) even in the line-of-sight (LoS) condition. In this paper, we analyze holographic MIMO performance with disk-shaped large intelligent surfaces (LISs) according to different precoding designs. Beyond the well-known technique of orbital angular momentum (OAM) of radio waves, we propose a new design based on polar Walsh functions. Furthermore, we characterize the performance gap between the proposed scheme and the optimal case with singular value decomposition (SVD) alongside perfect channel state information (CSI) as well as other benchmark schemes in terms of channel capacity. It is verified that the proposed scheme marginally underperforms the OAM-based approach, while offering potential perspectives for reducing implementation complexity and expenditure.Comment: Submission to ICEAA 202

Scalable Source Localization with Multichannel Alpha-Stable Distributions

International audienceIn this paper, we focus on the problem of sound source localization and we propose a technique that exploits the known and arbitrary geometry of the microphone array. While most probabilistic techniques presented in the past rely on Gaussian models, we go further in this direction and detail a method for source localization that is based on the recently proposed alpha-stable harmonizable processes. They include Cauchy and Gaussian as special cases and their remarkable feature is to allow a simple modeling of impulsive and real world sounds with few parameters. The approach we present builds on the classical convolutive mixing model and has the particularities of requiring going through the data only once, to also work in the underdetermined case of more sources than microphones and to allow massively parallelizable implementations operating in the time-frequency domain. We show that the method yields interesting performance for acoustic imaging in realistic simulations

Sketching for nearfield acoustic imaging of heavy-tailed sources

International audienceWe propose a probabilistic model for acoustic source localization with known but arbitrary geometry of the microphone array. The approach has several features. First, it relies on a simple nearfield acoustic model for wave propagation. Second, it does not require the number of active sources. On the contrary, it produces a heat map representing the energy of a large set of candidate locations, thus imaging the acoustic field. Second, it relies on a heavy-tail alpha-stable probabilistic model, whose most important feature is to yield an estimation strategy where the multichannel signals need to be processed only once in a simple on- line procedure, called sketching. This sketching produces a fixed-sized representation of the data that is then analyzed for localization. The resulting algorithm has a small computational complexity and in this paper, we demonstrate that it compares favorably with state of the art for localization in realistic simulations of reverberant environments

Réseaux à grand nombre de microphones : applicabilité et mise en œuvre

Recently, digital MEMS microphones came out and have opened new perspectives. One of them is the design of large-aperture and massively multichannel acoustical acquisition systems. Such systems meet good requirements for efficient source localization. However, new problems arise. First, an important data flow comes from the array, and must be processed fast enough. Second, if the large array is set up in situ, retrieving the position of numerous microphones becomes a challenging task. This thesis proposes methods addressing these two problems. The first part exhibits the description of the acquisition system, which has been developed during the thesis. First, we show that MEMS microphone characteristics are suitable for array processing applications. Then, real-time processing of channel signals is achieved by a parallel GPU implementation. This strategy is one solution to the heavy data flow processing issue. In this way, a real-time acoustic imaging tool was developed, and enables a dynamic wide-band diagnosis, for an arbitrary duration.The second part presents several robust geometric calibration methods: they retrieve microphone positions, based only on the array acoustic signals. Indeed, in real-life conditions, the state of the art methods are inefficient with large arrays. This thesis proposes techniques that guarantee the robustness of the calibration process. The proposed methods allow calibration in the different existing soundscapes, from free field to reverberant field. Various experimental scenarios prove the efficiency of the methods.L'apparition récente de microphones numériques MEMS a ouvert de nouvelles perspectives pour le développement de systèmes d'acquisition acoustiques massivement multi-canaux de grande envergure. De tels systèmes permettent de localiser des sources acoustiques avec de bonnes performances. En revanche, de nouvelles contraintes se posent. La première est le flux élevé de données issues de l'antenne, devant être traitées en un temps raisonnable. La deuxième contrainte est de connaître la position des nombreux microphones déployés in situ. Ce manuscrit propose des méthodes répondant à ces deux contraintes. Premièrement, une étude du système d'acquisition est présentée. On montre que les microphones MEMS sont adaptés pour des applications d'antennerie. Ensuite, un traitement en temps réel des signaux acquis via une implémentation parallèle sur GPU est proposé. Cette stratégie répond au problème de flux de données. On dispose ainsi d'un outil d'imagerie temps réel de sources large bande, permettant d'établir un diagnostic dynamique de la scène sonore.Deuxièmement, différentes méthodes de calibration géométrique pour la détermination de la position des microphones sont exposées. Dans des conditions réelles d'utilisation, les méthodes actuelles sont inefficaces pour des antennes étendues et à grand nombre de microphones. Ce manuscrit propose des techniques privilégiant la robustesse du processus de calibration. Les méthodes proposées couvrent différents environnements acoustiques réels, du champ libre au champ réverbérant. Leur efficacité est prouvée par différentes campagnes expérimentales

Implementation and applicability of very large microphone arrays

L'apparition récente de microphones numériques MEMS a ouvert de nouvelles perspectives pour le développement de systèmes d'acquisition acoustiques massivement multi-canaux de grande envergure. De tels systèmes permettent de localiser des sources acoustiques avec de bonnes performances. En revanche, de nouvelles contraintes se posent. La première est le flux élevé de données issues de l'antenne, devant être traitées en un temps raisonnable. La deuxième contrainte est de connaître la position des nombreux microphones déployés in situ. Ce manuscrit propose des méthodes répondant à ces deux contraintes. Premièrement, une étude du système d'acquisition est présentée. On montre que les microphones MEMS sont adaptés pour des applications d'antennerie. Ensuite, un traitement en temps réel des signaux acquis via une implémentation parallèle sur GPU est proposé. Cette stratégie répond au problème de flux de données. On dispose ainsi d'un outil d'imagerie temps réel de sources large bande, permettant d'établir un diagnostic dynamique de la scène sonore.Deuxièmement, différentes méthodes de calibration géométrique pour la détermination de la position des microphones sont exposées. Dans des conditions réelles d'utilisation, les méthodes actuelles sont inefficaces pour des antennes étendues et à grand nombre de microphones. Ce manuscrit propose des techniques privilégiant la robustesse du processus de calibration. Les méthodes proposées couvrent différents environnements acoustiques réels, du champ libre au champ réverbérant. Leur efficacité est prouvée par différentes campagnes expérimentales.Recently, digital MEMS microphones came out and have opened new perspectives. One of them is the design of large-aperture and massively multichannel acoustical acquisition systems. Such systems meet good requirements for efficient source localization. However, new problems arise. First, an important data flow comes from the array, and must be processed fast enough. Second, if the large array is set up in situ, retrieving the position of numerous microphones becomes a challenging task. This thesis proposes methods addressing these two problems. The first part exhibits the description of the acquisition system, which has been developed during the thesis. First, we show that MEMS microphone characteristics are suitable for array processing applications. Then, real-time processing of channel signals is achieved by a parallel GPU implementation. This strategy is one solution to the heavy data flow processing issue. In this way, a real-time acoustic imaging tool was developed, and enables a dynamic wide-band diagnosis, for an arbitrary duration.The second part presents several robust geometric calibration methods: they retrieve microphone positions, based only on the array acoustic signals. Indeed, in real-life conditions, the state of the art methods are inefficient with large arrays. This thesis proposes techniques that guarantee the robustness of the calibration process. The proposed methods allow calibration in the different existing soundscapes, from free field to reverberant field. Various experimental scenarios prove the efficiency of the methods

Sequential sensor placement using Bayesian compressive sensing for direction of arrival estimation

International audienceMeasurement selection is a general optimization problem with the purpose of minimizing the estimation error. It aims at answering the following question: which set of measurements will give us the best estimation? In our presentation, we propose a method to design an array for direction of arrival (DOA) estimation. Since the measurements are linked to a sensor, the optimization problem becomes a sensor placement problem. To solve it, a greedy data-dependent approach is chosen answering this new question: according to what is measured by the current array, which new sensor position could improve the DOA estimation at most? Concerning the DOA problem, we took inspiration from the compressed sensing (CS) framework, and consider the DOA estimation as a sparse localization problem. With this assumption, one can solve the DOA estimation problem in its undetermined form, by the Sparse Bayesian Inference (SBI). The algorithm estimates the source angles based on a sparsity-promoting hierarchical model. The next sensor choice minimizes a cost function related to the error covariance matrix. In Bayesian experimental design, a common choice is the D-design: it minimises the determinant of the error covariance matrix. In summary, the proposed strategy is a sequential sensor placement based on Bayesian experimental design. It alternates between a step of sparse DOA estimation, and a step to choose the sensor position in the D-optimal sense. The numerical experiments concern a DOA acoustic problem. The results show that an array designed by the proposed method needs less sensors than a random array in order to localize all sources

Sequential sensor selection for the localization of acoustic sources by sparse Bayesian learning

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