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Free-space antenna radiation pattern estimation from intensity measurements made in reverberating environments has been investigated by several authors. We extend our previous analysis of a simple directivity estimator appropriate to the case where the antenna under test (and the measuring probe) is located in an ideal reverberation chamber, where the reverberation-field components are Gaussian distributed and uncorrelated to a more realistic reverberation chamber model, where the field statistics deviate from the ideal ones. [ABSTRACT FROM AUTHOR]

We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored. In order to provide a proof of concept, we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained by P. Saulson and co-Workers to construct datasets of (windowed) transient waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian) noise with different signal-to-noise ratios (SNR). Principal component analysis (PCA) is next implemented for reducing data dimensionality, yielding results consistent with, and extending those in the literature. Then, a multilayer perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset, and used to classify the transients as glitch or burst. A Self-Organizing Map (SOM) architecture is finally used to classify the glitches. The glitch/burst discrimination and glitch classification abilities are gauged in terms of the related truth tables. Preliminary results suggest that the approach is effective and robust throughout the SNR range of practical interest. Perspective applications pertain both to distributed (network, multisensor) detection of GWBs, where some intelligence at the single node level can be introduced, and instrument diagnostics/optimization, where spurious transients can be identified, classified and hopefully traced back to their entry points. [ABSTRACT FROM AUTHOR]

Two locally optimum interferometer-network statistics for detecting unmodeled gravitational wave bursts in a non-Gaussian impulsive (glitchy) noise background are introduced. These follow from two possible approaches, where the sought transient signals are alternatively assumed as (i) unknown but deterministic or (ii) random. The first approach yields a locally optimum generalized likelihood ratio test detector, based on the maximum likelihood estimate of the gravitational wave signal. The second makes only minimal (least informative) assumptions about the sought signals, and yields a special version of Kassam's generalized-cross-correlation test detector. Both outperform the standard coherent network detector based on the Gaussian noise assumption. In the realistic case where the glitch noise distribution is non-stationary in time, or incompletely characterized, robust versions of these locally optimum network detectors can be effectively used. [ABSTRACT FROM AUTHOR]