The goal is, using a very large passive array, to determine the performance limits of a detector. The signal of interest is narrowband with a Gaussian envelope, and the contaminating noise is multivariate Class-A. Two different multivariate models for the Class A family are presented. One of the models is appropriate for array processing applications. The data is spatially dependent and temporally independent. It is shown, in the spatially independent case, that the Chernoff approximation does closely approximate the performance of the optimal detector. It is shown the approximation improves as the number of samples increases. Unfortunately, it is also shown that the Chernoff approximation requires numerical evaluation of a M-dimensional integral. For the application here, M may be as large as 150, ruling out this approach. Two alternative approaches are examined. First, approximating the Class A model by a Gaussian model is shown to result in a poor approximation. Second, the exact likelihood ratio is approximated by a piece-wise function. While the approximation can be done with very good accuracy, the bound must be evaluated numerically. 10 refs., 11 figs.

Department of Energy and Nuclear Regulatory Commission regulations governing material control and accountability in nuclear facilities have become more restrictive in the past decade, especially in areas that address the insider threat. As the insider threat receives greater credibility, regulations have been strengthened to increase the probability of detecting insider activity and to prevent removal of a significant quantity of Special Nuclear Material (SNM) from areas under control of the protective force.