A Gaussian process based approach for validation of multi-variable measurement systems: application to SAR measurement systems

TL;DR

This paper introduces a Gaussian Process-based validation method for multi-variable measurement systems, enabling efficient, system-agnostic, and comprehensive validation across large configuration spaces, demonstrated on SAR measurement systems.

Contribution

The paper presents a novel GP-based validation approach that is flexible, scalable, and applicable to various measurement systems without fixed benchmarks.

Findings

Effective validation of SAR measurement systems demonstrated

Approach is practical and adaptable to different measurement methods

Supports large configuration spaces and independent validation steps

Abstract

Resource-efficient and robust validation of systems designed to measure a multi-dimensional parameter space is an unsolved problem as it would require millions of test permutations for comprehensive validation coverage. In the paper, an efficient and comprehensive validation approach based on a Gaussian Process (GP) model of the test system has been developed that can operate system-agnostically, avoids calibration to a fixed set of known validation benchmarks, and supports large configuration spaces. The approach consists of three steps that can be performed independently by different parties: 1) GP model creation, 2) model confirmation, and 3) targeted search for critical cases. It has been applied to two systems that measure specific absorption rate (SAR) for compliance testing of wireless devices and apply different SAR measurement methods: a probe-scanning system (per IEC/IEEE 62209-1528), and a static sensor-array system (per IEC 62209-3). The results demonstrate that the approach is practical, feasible, suitable for proving effective equivalence, and can be applied to any measurement method and implementation. The presented method is sufficiently general to be of value not only for SAR system validation, but also in a wide variety of applications that require critical, independent, and efficient validation.