Nick Harold Klausner

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—The use of multiple disparate sonars allows one to exploit a high resolution sonar with good target definition while taking advantage of the clutter suppressing abilities of a low resolution broadband sonar co-registered over the same region to provide potentially much better detection and classification performance comparing to those of the single sonar(More)
—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 presents a coherence-based detection method for multiple disparate sensing systems using the multi-channel coherence analysis (MCA) framework. MCA provides an optimal coordinate system for multi-channel detection problems as it finds sets of one-dimensional mapping vectors that maximize the sum of the cross-correlations among all pair-wise(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)
—The use of multiple disparate platforms in many remote sensing and surveillance applications allows one to exploit the coherent information shared among all sensory systems thereby potentially reducing the risk of making single-sensory biased detection and classification decisions. This paper introduces a target detection method based upon multi-channel(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)