Optimum Quantization for Signal Detection

Abstract

Optimum quantization of data, primarily for signal detection applications, is considered. It is shown that two useful detection criteria lead to quantization which gives the minimum mean-squared error between the quantized output and the locally optimum nonlinear transform for each data sample. This criterion is an extension of the usual minimum distortion criterion for optimum quantizers. Numerical results show that it leads to optimum quantizers which can be considerably better in their performance for non-Guassian inputs than the minimum-distortion quantizers.