Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 10 of 10 for “"sensor array processing"”.
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Acoustic vector-sensor array processing
… theory yields useful performance criteria and processing techniques for acoustic pressure-sensor arrays. Acoustic vector-sensor arrays, which measure particle velocity and pressure, offer significant potential but require fundamental changes to algorithms and performance assessment. This thesis …
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Sensor Array Processing with Manifold Uncertainty
… realizable source locations for a given array configuration is referred to as the array manifold. In this thesis, array manifold ambiguities for linear arrays of omni-directional sensors in non-dispersive fields are considered. </p><p>First, the problem of underwater a hydrophone array …
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Robust vector sensor array processing and performance analysis
Acoustic vector sensors, which measure scalar pressure along with particle motion (a vector quantity), feature many advantages over omnidirectional hydrophone sensors. A sizable literature exists on the theory of processing signals for many vector sensor array applications. In practice, however, …
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Applied stochastic Eigen-analysis
… normal distribution. A longstanding problem in sensor array processing is addressed by designing an estimator for the number of signals in white noise that dramatically outperforms that proposed by Wax and Kailath. This methodology is extended to develop new parametric techniques for testing and …
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Applied stochastic eigen-analysis
… normal distribution. A longstanding problem in sensor array processing is addressed by designing an estimator for the number of signals in white noise that dramatically outperforms that proposed by Wax and Kailath. This methodology is extended to develop new parametric techniques for testing and …
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A unified framework for identifiability analysis in bilinear inverse problems
… lighting), blind phase and gain calibration in sensor array processing, and multichannel blind deconvolution (MBD). Applying our unified framework to BGPC, we derive sufficient conditions for unique recovery under several scenarios, including subspace, joint sparsity, and sparsity models. For …
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Efficient and guaranteed algorithms for sparse inverse problems
… the direction of arrival estimation problem in sensor array processing and later proposed and analyzed for joint sparse recovery by Feng and Bresler, provides a guarantee with the minimum number of measurements. We focus instead on the unfavorable but practically significant case of rank defect …
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Highly Robust and Efficient Estimators of Multivariate Location and Covariance with Applications to Array Processing and Financial Portfolio Optimization
Throughout stochastic data processing fields, mean and covariance matrices are commonly employed for purposes such as standardizing multivariate data through decorrelation. For practical applications, these matrices are usually estimated, and often, the data used for these estimates are …
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Bilinear inverse problems with sparsity: Optimal identifiability conditions and efficient recovery
Bilinear inverse problems (BIPs), the resolution of two vectors given their image under a bilinear mapping, arise in many applications. Without further constraints, BIPs are usually ill-posed. In practice, parsimonious structures of natural signals (e.g., subspace or sparsity) are exploited. …
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Audio computing in the wild: frameworks for big data and small computers
… in a small device that has to process various sensor signals in real time; a lightweight custom chipset for speech enhancement on hand-held devices; instant music analysis engine running on smartphone apps. In all those applications, efficient machine learning algorithms are supposed to achieve …