BER Performance Improvement with Joint Angle-Delay-Polarization Estimation of Multipath Channel Parameters

International audienceIn mobile telecommunications, the quality of demodulation is strongly impacted by channel estimation. The Joint Angle, Delay and Polarization Estimation (JADPE) problem is addressed in this paper when a linear uniform array of crossed dipoles is used to measure the signal. The purpose of this paper is to study the high resolution JADPE ESPRIT method for the estimation of the parameters that characterize each path in order to improve channel estimation and demodulation. A complete system simulation (based on TDD-UTRA standard of UMTS) is proposed in order to evaluate performance in terms of channel estimation accuracy and BER. Simulation results show the interest of considering the polarization as a channel parameter in order to increase the performance. Improvement in the path separation and received signal power is obtained and, because of this, channel estimation accuracy and data estimation accuracy are improved as well

Interpolation et méthodes à haute résolution pour antennes non uniformes

Le travail présenté dans ce papier se rapporte à l'application et le développement de méthodes de localisation de sources pour les antennes non uniformes. Il s'agit en particulier de l'adaptation des méthodes à haute résolution pour les Antennes Linéaires Non Uniformes (ALNU) afin de réaliser l'estimation de direction d'arrivée. Pour se faire une méthode d'interpolation spatiale est mise en oeuvre. L'objet de ce papier est de comparer les performances de ces différentes méthodes et d'évaluer la sensibilité des méthodes au choix des paramètres de l'interpolateur

Performance analysis of MUSIC for spatially distributed sources

In this paper, the direction of arrival (DOA) localization of spatially distributed sources impinging on a sensor array is considered. The performance of the well known MUSIC estimator is studied in presence of model errors due to angular dispersion of sources. Taking into account the coherently distributed source model proposed in [1], we establish closed-form expressions of the DOA estimation error and mean square error (MSE) due to both the model errors and the effects of a finite number of snapshots. The analytical results are validated by numerical simulations and allow to analyze the performance of MUSIC for coherently distributed sources

Borne de Cramér-Rao pour la conception de réseaux de capteurs en présence des sources dispersées

National audienceIn array signal processing the signals impinging on the array are often assumed to come from point sources. However, in some applications, for example, in aero-acoustic imaging [1], the angular dispersion is no longer negligible and a model for distributed sources could be more appropriate. In this paper, focusing on the distributed source model, we derive an approximated expression of the Cramér-Rao bound in the case of one distributed source. In addition, we find that the component of the inter-correlation of the direction of arrival and the angular dispersion parameter of the source can be canceled by some particular geometries of the antenna array. The simulation results validate the theoretical results.Le modèle de source ponctuelle est couramment utilisé pour le traitement d'antenne, néanmoins, certaines applications physiques comme, par exemple, l'imagerie aéro-acoustique [1], n'obéissent pas à cette hypothèse, car la dispersion angulaire de la source n'est pas négligeable. Dans cet article, nous proposons une expression approchée de la borne de Cramér-Rao (BCR) dans le cas d'une source dispersée. En outre, nous montrons que la composante d'inter-corrélation de la direction d'arrivée (DDA) et du paramètre de dispersion angulaire de la source peut être réduite, voire annulée par des géométries particulières du réseau de capteurs. Les résultats de simulations numériques sont en adéquation avec les résultats théoriques

EM-ESPRIT ALGORITHM FOR DIRECTION FINDING WITH NONUNIFORM ARRAYS

International audienceThis paper deals with the problem of the Direction Of Arrival (DOA) estimation with nonuniform linear arrays. The proposed method is a combination of the Expectation Maximization (EM) and the ESPRIT methods. The EM algorithm interpolates the nonuniform array to an equivalent uniform array, and then, the application of ESPRIT is possible, in order to estimate the DOA. One of this method novelties lies in its capacity of dealing with any nonuniform array geometry. This technique manifests significant performance and computational advantages over previous algorithms such as MUSIC, specially in the preasymptotic domain, and the comparison with the theoretical Cramer-Rao Bounds (CRB) shows its efficiency

Localisation des sources distribuées en champ proche

National audienceLa plupart des algorithmes du traitement d'antennes ont été développés avec l'hypothèse de sources ponctuelles situées en champ lointain. Certaines applications physiques n'obéissent pas à cet hypothèse, ainsi l'extension angulaire en champ proche doit être prise en compte dans le modèle. Dans ce papier, on propose un modèle généralisé pour la caractérisation des sources ayant une extension angulaire dans un champ proche. Nous proposons ensuite un algorithme d'estimation conjointe de la direction d'arrivée nominale, de la dispersion angulaire autour de cette direction et de la distance séparant la source de l'antenne. La méthode est basée sur une généralisation de l'estimateur MUSIC sur le principe de la minimisation d'un produit scalaire entre un vecteur fonction du vecteur directeur et le vecteur propre bruit de la matrice de corrélation. Nous comparons notre méthode avec un estimateur MUSIC conventionnel (source ponctuelle en champ proche). Les résultats montrent que le nouvel estimateur est plus performant en réduisant l'erreur quadratique moyenne des estimés pour les sources distribuées en champ proche. L'estimateur proposé est comparé avec la borne de Cramer-Rao (BCR)

A Bayesian sparse inference approach in near-field wideband aeroacoustic imaging

International audienceRecently improved deconvolution methods using sparse regularization achieve high spatial resolution in aeroacoustic imaging in the low Signal-to-Noise Ratio (SNR), but sparse prior and model parameters should be optimized to obtain super resolution and be robust to sparsity constraint. In this paper, we propose a Bayesian Sparse Inference Approach in Aeroacoustic Imaging (BSIAAI) to reconstruct both source powers and positions in poor SNR cases, and simultaneously estimate background noise and model parameters. Double Exponential prior model is selected for source spatial distribution and hyper-parameters are estimated by Joint Maximized A Posterior criterion and Bayesian Expectation and Minimization algorithm. On simulated and wind tunnel data, proposed approach is well applied for near-field wideband monopole and extended source imaging. Comparing to several classical methods, proposed approach is robust to noise, super resolution, wide dynamic range, and source number and SNR are not needed

Localization of Spatially Distributed Near-Field Sources with Unknown Angular Spread Shape

International audienceIn this paper, we propose to localize and characterize coherently distributed (CD) sources in near-field. Indeed, it appears that in some applications, the more the sources are close to the array of sensors, the more they can seem scattered. It thus appears of the biggest importance to take into account the angular distribution of the sources in the joint direction of arrival (DOA) and range estimation methods. The methods of the literature which consider the problem of distributed sources do not handle with the case of near field sources and require that the shape of the dispersion is known. The main contribution of the proposed method is to estimate the shape of the angular distribution using an additional shape parameter to address the case of unknown distributions. We propose to jointly estimate the DOA, the range, the spread angle and the shape of the spread distribution. Accurate estimation is then achieved even when the shape of the angular spread distribution is unknown or imperfectly known. Moreover, the proposed estimator improves angular resolution of the sources

Bayesian sparse regularization in near-field wideband aeroacoustic imaging for wind tunnel test

International audienc

Sparsity-based localization of spatially coherent distributed sources

International audienceIn this paper, the localization of spatially distributed sources is considered. Based on the problem formulation of the De-convolution Approach for the Mapping of Acoustic Sources (DAMAS), a criterion based on a convex optimization under sparsity constraint is proposed to locate the sources. Also an original method is given to recover the angular distributions and the power of the sources. Simulations executed in the scenario of a mixture of distributed and point sources illustrate the validation of the proposed approach compared to other methods