High-Rate Vector Quantization for the Neyman-Pearson Detection of Correlated Processes

TL;DR

This paper analyzes how high-rate vector quantization affects Neyman-Pearson detection performance for correlated processes, providing a closed-form expression for the error exponent and demonstrating improved detection accuracy with optimized quantization strategies.

Contribution

It extends previous work to correlated observations, deriving a compact formula for the error exponent in high-rate quantization, and identifies strategies for enhanced detection performance.

Findings

Error probability converges exponentially to zero with increasing samples.

Error exponent scales as N^{2/d} in high-rate quantization.

Optimized quantization strategies improve detection performance.

Abstract

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.