  We consider the problem of detecting a burst signal of unknown shape. We
introduce a statistic which generalizes the excess power statistic proposed by
Flanagan and Hughes and extended by Anderson et al. The statistic we propose is
shown to be optimal for arbitrary noise spectral characteristic, under the two
hypotheses that the noise is Gaussian, and that the prior for the signal is
uniform. The statistic derivation is based on the assumption that a signal
affects only affects N samples in the data stream, but that no other
information is a priori available, and that the value of the signal at each
sample can be arbitrary. We show that the proposed statistic can be implemented
combining standard time-series analysis tools which can be efficiently
implemented, and the resulting computational cost is still compatible with an
on-line analysis of interferometric data. We generalize this version of an
excess power statistic to the multiple detector case, also including the effect
of correlated noise. We give full details about the implementation of the
algorithm, both for the single and the multiple detector case, and we discuss
exact and approximate forms, depending on the specific characteristics of the
noise and on the assumed length of the burst event. As a example, we show what
would be the sensitivity of the network of interferometers to a delta-function
burst.
