The blind source separation problem has received a great deal of attention in previous years. The aim of this problem is to estimate a set of original source signals from a set of linearly mixed signals through any number of signal processing techniques. While many methods exist that attempt to solve the blind source separation problem, a new technique is being used that uniquely separates audio sources as they are received from a microphone array. In this thesis a new algorithm is proposed that that utilizes the ICA algorithm in conjunction with a filtering technique that separates source signals and then removes sources of interference so that a signal of interest can be accurately tracked. Experimental results will compare a common blind source separation technique to the new algorithm and show that the new algorithm can detect a signal of interest and accurately track it as it moves through an anechoic environment. / Master of Science

This thesis describes the design and implementation of a 4-channel microphone array that is an adaptive beamformer used for hands-free telephony in a noisy environment. The microphone signals are amplified, then sent to an A/D converter. The microprocessor board takes the data from the 4 channels and utilizes digital signal processing to determine the direction-of-arrival of the sources and create an output which 'steers' the microphone array to the desired look direction while trying to minimize the energy of interference sources and noise. All of the processing for this thesis will be done on a computer using MATLAB. The MUSIC algorithm is used for direction finding in this thesis. It is shown to be effective in estimating direction-of-arrival for 1 speech source and 2 speech sources that are spaced fairly apart, with significant results down to a -5 dB SNR even. The MUSIC algorithm requires knowledge of the number of sources a priori, requiring an estimator for the number of sources. Though proposed estimators for the number of sources were examined, an effective estimator was not encountered for the case where there are multiple speech sources. Beamforming methods are examined which utilize knowledge of the source direction-of-arrival from the MUSIC algorithm. The input is split into 6 subbands such that phase-steered beamforming would be possible. Two methods of phase-steered beamforming are compared in both narrowband and wideband scenarios, and it is shown that phase-steering the array to the desired source direction-of-arrival has about 0.3 dB better beamforming performance than the simple time-delay steered beamformer using no subbands. As the beamforming solution is inadequate to achieve desired results, a generalized sidelobe canceler (GSC) is developed which incorporates a beamformer. The sidelobe canceler is evaluated using both NLMS and RLS adaptation. The RLS algorithm inherently gives better results than the NLMS algorithm, though the computational complexity renders the solution impractical for implementation with today's technology. A testing setup is presented which involves a linear 4-microphone array connected to a DSP chip that collects the data. Tests were done using 1 speech source and a model of the car noise environment. The sidelobe canceler's performance using 6 subbands (phase-delay GSC) and using 1 band (time-delay GSC) with NLMS updating are compared. The overall SNR improvement is determined from the signal and noise input and output powers, with signal-only as the input and noise-only as the input to the GSC. The phase-delay GSC gives on average 7.4 dB SNR improvement while the time-delay GSC gives on average 6.2 dB SNR improvement. / Master of Science

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Robust adaptive beamforming using worst-case performance optimization is developed in recent years. It had good performance against array response errors, but it cannot reject strong interferences. In this paper, we propose a scheme for robust adaptive beamforming with broad nulls to reject strong interferences. We add a quadratic constraint to suppress the power of the array response over a spatial region of the interferences. The optimal weighting vector is then obtained by minimizing the power of the array output subject to quadratic constrains on the desired signal and interferences, respectively. We derive the formulations for the optimization problem and solve it efficiently using Newton recursive algorithm. Numerical examples are presented to compare the performances of the robust adaptive beamforming with no null constrains, sharp nulls and broad nulls. The results show its powerful ability to reject strong interferences.

Time-Frequency (TF) masking is an audio processing technique useful for isolating an audio source from interfering sources. TF masking has been applied and studied in monaural and binaural applications, but has only recently been applied to distributed microphone arrays. This work focuses on evaluating the TF masking technique's ability to isolate human speech and improve speech intelligibility in an immersive "cocktail party" environment. In particular, an upper-bound on TF masking performance is established and compared to the traditional delay-sum and general sidelobe canceler (GSC) beamformers. Additionally, the novel technique of combining the GSC with TF masking is investigated and its performance evaluated. This work presents a resource-efficient method for studying the performance of these isolation techniques and evaluates their performance using both virtually simulated data and data recorded in a real-life acoustical environment. Further, methods are presented to analyze speech intelligibility post-processing, and automated objective intelligibility measurements are applied alongside informal subjective assessments to evaluate the performance of these processing techniques. Finally, the causes for subjective/objective intelligibility measurement disagreements are discussed, and it was shown that TF masking did enhance intelligibility beyond delay-sum beamforming and that the utilization of adaptive beamforming can be beneficial.

Approved for public release; distribution is unlimited / Computer simulation studies of a frequency domain adaptive beamforming algorithm are presented. These simulation studies were conducted to determine the multiple broadband target localization capability and the full angular coverage capability of the algorithm. The algorithm was evaluated at several signal-to-noise ratios with varying sampling rates. The number of iterations that the adaptive algorithm took to reach a minimum estimation error was determined. Results of the simulation studies indicate that the algorithm can localize multiple broadband targets and has full angular coverage capability. / http://archive.org/details/computersimulati00behr / Lieutenant, United States Navy

Adaptive heal-doming can significantly improve the image quality in biomedical ultrasound by reducing the clutter due to interfering signals arriving from undesired directions. Adaptive beamforming is computationally expensive, and the objective of this thesis is to expose and explore tradeoffs between computational complexity and quality of adaptive beamforming. We consider the conventional linearly constrained minimum variance (LCMV) adaptive beamformer, applied to B-mode ultrasound imaging, and study an alternative based on the well-known generalized sidelobe canceller (GSC) whose adaptation relies on unconstrained gradient-driven optimization. To our knowledge, this is the first time a GSC-based gradient-driven approach has been applied and evaluated in the context of ultrasound beamforming. As another alternative to the conventional LCMV method, we also propose and evaluate a simple idea of updating the beamformer's weight vector at a reduced rate. Both approaches have lead to significant computational savings, but they also sacrifice beamforming optimality. Our simulations show that despite suboptimal beamforming. the ultrasound image quality remains acceptable.

L'imagerie ultrasonore (US) permet de réaliser des examens médicaux non invasifs avec des méthodes d'acquisition rapides à des coûts modérés. L'imagerie cardiaque, abdominale, fœtale, ou mammaire sont quelques-unes des applications où elle est largement utilisée comme outil de diagnostic. En imagerie US classique, des ondes acoustiques sont transmises à une région d'intérêt du corps humain. Les signaux d'écho rétrodiffusés, sont ensuite formés pour créer des lignes radiofréquences. La formation de voies (FV) joue un rôle clé dans l'obtention des images US, car elle influence la résolution et le contraste de l'image finale. L'objectif de ce travail est de modéliser la formation de voies comme un problème inverse liant les données brutes aux signaux RF. Le modèle de formation de voies proposé ici améliore le contraste et la résolution spatiale des images échographiques par rapport aux techniques de FV existants. Dans un premier temps, nous nous sommes concentrés sur des méthodes de FV en imagerie US. Nous avons brièvement passé en revue les techniques de formation de voies les plus courantes, en commencent par la méthode par retard et somme standard puis en utilisant les techniques de formation de voies adaptatives. Ensuite, nous avons étudié l'utilisation de signaux qui exploitent une représentation parcimonieuse de l'image US dans le cadre de la formation de voies. Les approches proposées détectent les réflecteurs forts du milieu sur la base de critères bayésiens. Nous avons finalement développé une nouvelle façon d'aborder la formation de voies en imagerie US, en la formulant comme un problème inverse linéaire liant les échos réfléchis au signal final. L'intérêt majeur de notre approche est la flexibilité dans le choix des hypothèses statistiques sur le signal avant la formation de voies et sa robustesse dans à un nombre réduit d'émissions. Finalement, nous présentons une nouvelle méthode de formation de voies pour l'imagerie US basée sur l'utilisation de caractéristique statistique des signaux supposée alpha-stable. / Ultrasound (US) allows non-invasive and ultra-high frame rate imaging procedures at reduced costs. Cardiac, abdominal, fetal, and breast imaging are some of the applications where it is extensively used as diagnostic tool. In a classical US scanning process, short acoustic pulses are transmitted through the region-of-interest of the human body. The backscattered echo signals are then beamformed for creating radiofrequency(RF) lines. Beamforming (BF) plays a key role in US image formation, influencing the resolution and the contrast of final image. The objective of this thesis is to model BF as an inverse problem, relating the raw channel data to the signals to be recovered. The proposed BF framework improves the contrast and the spatial resolution of the US images, compared with the existing BF methods. To begin with, we investigated the existing BF methods in medical US imaging. We briefly review the most common BF techniques, starting with the standard delay-and-sum BF method and emerging to the most known adaptive BF techniques, such as minimum variance BF. Afterwards, we investigated the use of sparse priors in creating original two-dimensional beamforming methods for ultrasound imaging. The proposed approaches detect the strong reflectors from the scanned medium based on the well-known Bayesian Information Criteria used in statistical modeling. Furthermore, we propose a new way of addressing the BF in US imaging, by formulating it as a linear inverse problem relating the reflected echoes to the signal to be recovered. Our approach offers flexibility in the choice of statistical assumptions on the signal to be beamformed and it is robust to a reduced number of pulse emissions. At the end of this research, we investigated the use of the non-Gaussianity properties of the RF signals in the BF process, by assuming alpha-stable statistics of US images.

Presented herein is the design and construction process in the expansion of BYU's seven-element experimental platform to a nineteen-element platform for phased array feed experiments. The nineteen-element system was deployed at the National Radio Astronomy Observatory (NRAO) in Green Bank West Virginia for use on the Green Bank 20-Meter Telescope. Numerical simulations were performed to determine how sensitivity was affected by electronic beam steering and offset of the phased array feed (PAF) relative to the focal plane of the reflector. These simulated results were then compared to experimental data.

No description available.

The detection and localization of important acoustic events in a complex urban environment, such as gunfire and explosions, is critical to providing effective surveillance of military and civilian areas and installations. In a complex environment, obstacles such as terrain or buildings introduce multipath propagations, reflections, and diffractions which make source localization challenging. This dissertation focuses on the problem of source localization in three-dimensional (3D) realistic urban environments. Two different localization techniques are developed to solve this problem: a) Beamforming using a few microphone phased arrays in conjunction with a high fidelity model and b) Fingerprinting using many dispersed microphones in conjunction with a low fidelity model of the environment. For an effective source localization technique using microphone phased arrays, several candidate beamformers are investigated using 2D and corresponding 3D numerical models. Among them, the most promising beamformers are chosen for further investigation using 3D large models. For realistic validation, localization error of the beamformers is analyzed for different levels of uncorrelated noise in the environment. Multiple-array processing is also considered to improve the overall localization performance. The sensitivity of the beamformers to uncertainties that cannot be easily accounted for (e.g. temperature gradient and unmodeled object) is then investigated. It is observed that evaluation in 3D models is critical to assess correctly the potential of the localization technique. The enhanced minimum variance distortionless response (EMVDR) is identified to be the only beamformer that has super-directivity property (i.e. accurate localization capability) and still robust to uncorrelated noise in the environment. It is also demonstrated that the detrimental effect of uncertainties in the modeling of the environment can be alleviated by incoherent multiple arrays. For efficient source localization technique using dispersed microphones in the environment, acoustic fingerprinting in conjunction with a diffused-based energy model is developed as an alternative to the beamforming technique. This approach is much simpler requiring only microphones rather than arrays. Moreover, it does not require an accurate modeling of the acoustic environment. The approach is validated using the 3D large models. The relationship between the localization accuracy and the number of dispersed microphones is investigated. The effect of the accuracy of the model is also addressed. The results show a progressive improvement in the source localization capabilities as the number of microphones increases. Moreover, it is shown that the fingerprints do not need to be very accurate for successful localization if enough microphones are dispersed in the environment. / Ph. D.