Abstract
Wavelet analysis is known to be a good option for change detection in many contexts. Detecting changes in solution volumes that are measured with both additive and relative error is an important aspect of safeguards for facilities that process special nuclear material. This paper qualitatively compares wavelet-based change detection to a lag-one differencing option using realistic simulated solution volume data for which the true change points are known. We then show quantitatively that Haar wavelet-based change detection is effective for finding the approximate location of each change point, and that a simple piecewise linear optimization step is effective to refine the initial wavelet-based change point estimate.
Highlights
As a component of a safeguards system for nuclear material, solutions in a nuclear facility such as an aqueous reprocessing plant are monitored for change
We show quantitatively that Haar wavelet-based change detection is effective for finding the approximate location of each change point, and that a simple piecewise linear optimization step is effective to refine the initial wavelet-based change point estimate
It is known that wavelet change detection as well as other change detection options can identify the location of change points, but any method’s performance will suffer as the signal-to-noise ratio decreases
Summary
As a component of a safeguards system for nuclear material, solutions in a nuclear facility such as an aqueous reprocessing plant are monitored for change. A change in the measured tank volume over time indicates loss or gain of nuclear material. Such changes could be part of legal plant operation, or could indicate a diversion of nuclear material by an adversary. In applications of wavelets for change detection, wavelet decomposition coefficients have been used to identify edges in images and change points in time series data [2,3]. Other statistical methods are available for change point detection in the SM application, such as monitoring successive differences of the time series. We show quantitatively that Haar wavelet-based change detection is effective for finding the approximate location of each change point, and that a simple piecewise linear optimization step is effective to refine the initial wavelet-based change point estimate
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