  We propose a sequential nonparametric test for detecting a change in
distribution, based on windowed Kolmogorov--Smirnov statistics. The approach is
simple, robust, highly computationally efficient, easy to calibrate, and
requires no parametric assumptions about the underlying null and alternative
distributions. We show that both the false-alarm rate and the power of our
procedure are amenable to rigorous analysis, and that the method outperforms
existing sequential testing procedures in practice. We then apply the method to
the problem of detecting radiological anomalies, using data collected from
measurements of the background gamma-radiation spectrum on a large university
campus. In this context, the proposed method leads to substantial improvements
in time-to-detection for the kind of radiological anomalies of interest in
law-enforcement and border-security applications.
