Wideband DOA Estimation via Sparse Bayesian Learning over a Khatri-Rao Dictionary

This paper deals with the wideband direction-of-arrival (DOA) estimation by exploiting the multiple measurement vectors (MMV) based sparse Bayesian learning (SBL) framework. First, the array covariance matrices at different frequency bins are focused to the reference frequency by the conventional focusing technique and then transformed into the vector form. Then a matrix called the Khatri-Rao dictionary is constructed by using the Khatri-Rao product and the multiple focused array covariance vectors are set as the new observations. DOA estimation is to find the sparsest representations of the new observations over the Khatri-Rao dictionary via SBL. The performance of the proposed method is compared with other well-known focusing based wideband algorithms and the Cramer-Rao lower bound (CRLB). The results show that it achieves higher resolution and accuracy and can reach the CRLB under relative demanding conditions. Moreover, the method imposes no restriction on the pattern of signal power spectral density and due to the increased number of rows of the dictionary, it can resolve more sources than sensors

The influence of random element displacement on DOA estimates obtained with (Khatri-Rao-)root-MUSIC

Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the error on DOA estimates due to random errors in the array geometry. Therefore, we propose a stochastic collocation method that relies on a generalized polynomial chaos expansion to connect the statistical distribution of random position errors to the resulting distribution of the DOA estimates. We apply this technique to the conventional root-MUSIC and the Khatri-Rao-root-MUSIC methods. According to Monte-Carlo simulations, this novel approach yields a speedup by a factor of more than 100 in terms of CPU-time for a one-dimensional case and by a factor of 56 for a two-dimensional case

Detection of Wideband Signal Number Based on Bootstrap Resampling

Knowing source number correctly is the precondition for most spatial spectrum estimation methods; however, many snapshots are needed when we determine number of wideband signals. Therefore, a new method based on Bootstrap resampling is proposed in this paper. First, signals are divided into some nonoverlapping subbands; apply coherent signal methods (CSM) to focus them on the single frequency. Then, fuse the eigenvalues with the corresponding eigenvectors of the focused covariance matrix. Subsequently, use Bootstrap to construct the new resampling matrix. Finally, the number of wideband signals can be calculated with obtained vector sequences according to clustering technique. The method has a high probability of success under low signal to noise ratio (SNR) and small number of snapshots

Panorama des opérateurs de focalisation et applications

Au cours de ces dernières années, plusieurs méthodes de traitement de signaux à large bande ont été proposées. Ces méthodes sont une extension des algorithmes de signaux bande étroite. Elles sont basées essentiellement sur la technique de focalisation. La recherche d'une amélioration du rapport signal sur bruit a conduit au développement de nombreux opérateurs de focalisation. Certains travaux ont montré que les opérateurs unitaires sont optimaux. Ces opérateurs sont construits à partir des vecteurs propres des matrices interspectrales des signaux enregistrés. L'objectif principal de ces opérateurs est de conserver la propriété du bruit blanc après focalisation. Dans cette étude, nous nous proposons de nouveaux opérateurs de focalisation en présence d'un bruit gaussien de structure de corrélation spatiale inconnue. Pour cela nous utilisons les vecteurs propres de la matrice des cumulants des signaux reçus. La focalisation utilisant les opérateurs proposés améliore considérablement le rapport signal sur bruit et permet une meilleure localisation de sources. De plus elle conduit à une réduction du temps de calcul comparée aux méthodes classiques. Nous avons comparé les divers algorithmes en utilisant des données réelles d'acoustique sous-marine. Les résultats obtenus montrent l'efficacité, en terme de localisation, des algorithmes proposés

Méthode haute résolution large bande pour la localisation d'objets enfouis

Cette étude traite le problème de la localisation d'objets enfouis en utilisant une source acoustique. Nous proposons une nouvelle méthode qui incorpore la solution exacte du champ réfléchi dans la méthode MUSIC et qui utilise le lissage fréquentiel pour décorréler les signaux afin d'estimer les directions d'arrivée des signaux réfléchis et les distances obliques entre l'antenne et les objets à localiser. Les performances de la méthode proposée sont validées sur des données expérimentales enregistrées durant des expériences d'acoustique sous-marine

Focusing Operators and Tracking Moving Wideband Sources , Journal of Telecommunications and Information Technology, 2016, nr 4

In this paper, the localization of wideband source with an algorithm to track a moving source is investigated. To locate the wideband source, the estimation of two directions of arrival (DOA) of this source from two different arrays of sensors is used, and then, a recursive algorithm is applied to predict the moving source’s position. The DOA is estimated by coherent subspace methods, which use the focusing operators. Practical methods of the estimation of the coherent signal subspace are given and compared. Once the initial position is estimated, an algorithm of tracking the moving source is presented to predict its trajectory

EM Algorithm for Multiple Wideband Source Localization

A computationally efficient algorithm using the expectation-maximization (EM) algorithm for multiple wideband source localization in the near field of a sensor array/area is addressed in this thesis. Our idea is to decompose the observed sensor data, which is a superimposition of multiple sources, into the individual components in the frequency domain and then estimate the corresponding location parameters associated with each component separately. Instead of the conventional alternating projection (AP) method, we propose to adopt the EM algorithm in this work; our new method involves two steps, namely Expectation (E-step) and Maximization (M-step). In the E-step, the individual incident source waveforms are estimated. Then, in the M-step, the maximum likelihood estimates of the source location parameters are obtained. These two steps are executed iteratively and alternatively until the pre-defined convergence is reached. The computational complexity comparison between our proposed EM algorithm and the existing AP scheme is investigated. It is shown through Monte Carlo simulations that the computational complexity of the proposed EM algorithm is significantly lower than that of the existing AP algorithm