  This paper investigates the effect of quantization on the performance of the
Neyman-Pearson test. It is assumed that a sensing unit observes samples of a
correlated stationary ergodic multivariate process. Each sample is passed
through an N-point quantizer and transmitted to a decision device which
performs a binary hypothesis test. For any false alarm level, it is shown that
the miss probability of the Neyman-Pearson test converges to zero exponentially
as the number of samples tends to infinity, assuming that the observed process
satisfies certain mixing conditions. The main contribution of this paper is to
provide a compact closed-form expression of the error exponent in the high-rate
regime i.e., when the number N of quantization levels tends to infinity,
generalizing previous results of Gupta and Hero to the case of non-independent
observations. If d represents the dimension of one sample, it is proved that
the error exponent converges at rate N^{2/d} to the one obtained in the absence
of quantization. As an application, relevant high-rate quantization strategies
which lead to a large error exponent are determined. Numerical results indicate
that the proposed quantization rule can yield better performance than existing
ones in terms of detection error.
