  This work considers the problem of quickest detection of signals in a coupled
system of $N$ sensors, which receive continuous sequential observations from
the environment. It is assumed that the signals, which are modeled by general
It\^{o} processes, are coupled across sensors, but that their onset times may
differ from sensor to sensor. Two main cases are considered; in the first one
signal strengths are the same across sensors while in the second one they
differ by a constant. The objective is the optimal detection of the first time
at which any sensor in the system receives a signal. The problem is formulated
as a stochastic optimization problem in which an extended minimal
Kullback-Leibler divergence criterion is used as a measure of detection delay,
with a constraint on the mean time to the first false alarm. The case in which
the sensors employ cumulative sum (CUSUM) strategies is considered, and it is
proved that the minimum of $N$ CUSUMs is asymptotically optimal as the mean
time to the first false alarm increases without bound. In particular, in the
case of equal signal strengths across sensors, it is seen that the difference
in detection delay of the $N$-CUSUM stopping rule and the unknown optimal
stopping scheme tends to a constant related to the number of sensors as the
mean time to the first false alarm increases without bound. Alternatively, in
the case of unequal signal strengths, it is seen that this difference tends to
zero.
