Nick Harold Klausner

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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 multichannel data to analyze their linear dependence or coherence. This decomposition then allows one to extract MCA features which can be(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)
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 considers the problem of testing for the independence among multiple (≥ 2) 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(More)
This letter considers the problem of threshold selection for a correlation test among multiple (≥2) random vectors. The generalized likelihood ratio test (GLRT) for this problem uses a generalized Hadamard ratio to test for block diagonality in a composite covariance matrix. As the number of realizations used to estimate the composite covariance(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)
K-SVD method has recently been introduced to learn a specific dictionary matrix that best fits a set of training data vectors. K-SVD is flexible in that any preferred pursuit method of sparse coding can be used to represent the data. In this paper, we show how K-SVD method can be used in conjunction with a fast orthogonal matching pursuit implemented using(More)
There are many examples of oceanic applications that rely on the observations from multiple disparate sensing systems to detect various sources of interest. One example is the detection of underwater mine-like objects in multiple side-scan images generated from sonar systems that could be disparate in location, frequency, beamwidth resolution, etc. Typical(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 multivariate 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)