Nick Harold Klausner

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—This paper addresses the problem of testing for the independence among multiple (≥ 2) random vectors. The Generalized Likelihood Ratio Test tests the null hypothesis that the composite covariance matrix of the channels is block-diagonal, using a generalized Hadamard ratio. Using the theory of Gram determinants, we show that this Hadamard ratio is(More)
—This paper introduces a new target detection method for multiple disparate sonar platforms. The detection method is based upon multi-channel coherence analysis (MCA) framework which allows one to optimally decompose the multi-channel data to analyze their linear dependence or coherence. This decomposition then allows one to extract MCA features which can(More)
—This paper uses the canonical correlation decomposition (CCD) framework to investigate the spatial correlation of sources captured using two spatially separated sensor arrays. The relationship between the canonical correlations of the observed signals and the spatial correlation coefficients of the source signals are first derived, including an analysis of(More)
This paper considers the problem of testing for the independence among multiple random vectors with each random vector representing a time series captured at one sensor. Implementing the Generalized Likelihood Ratio Test involves testing the null hypothesis that the composite covariance matrix of the channels is block-diagonal through the use of a(More)
This paper investigates the effects of incrementally adding new data to the classical Gauss-Gauss detector for testing between the known covariance matrices in competing multivari-ate models. We show that updating the likelihood ratio and J-divergence as a result of general data augmentation inherently involves linearly estimating the new data from the old.(More)
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