Measurement-driven Quality Assessment of Nonlinear Systems by Exponential Replacement

TL;DR

This paper introduces a measurement-driven approach to assess the quality of nonlinear systems by approximating their output distribution with an exponential family, ensuring conservative estimates of system information content.

Contribution

It proposes a novel empirical method using first two moments to approximate nonlinear system outputs with exponential families, guaranteeing pessimistic Fisher information estimates.

Findings

The method provides conservative Fisher information estimates.

It effectively approximates nonlinear system output distributions.

Practical examples demonstrate its applicability in quality assessment.

Abstract

We discuss the problem how to determine the quality of a nonlinear system with respect to a measurement task. Due to amplification, filtering, quantization and internal noise sources physical measurement equipment in general exhibits a nonlinear and random input-to-output behaviour. This usually makes it impossible to accurately describe the underlying statistical system model. When the individual operations are all known and deterministic, one can resort to approximations of the input-to-output function. The problem becomes challenging when the processing chain is not exactly known or contains nonlinear random effects. Then one has to approximate the output distribution in an empirical way. Here we show that by measuring the first two sample moments of an arbitrary set of output transformations in a calibrated setup, the output distribution of the actual system can be approximated by an equivalent exponential family distribution. This method has the property that the resulting approximation of the statistical system model is guaranteed to be pessimistic in an estimation theoretic sense. We show this by proving that an equivalent exponential family distribution in general exhibits a lower Fisher information measure than the original system model. With various examples and a model matching step we demonstrate how this estimation theoretic aspect can be exploited in practice in order to obtain a conservative measurement-driven quality assessment method for nonlinear measurement systems.