Efficient Determination of Reverberation Chamber Time Constant

TL;DR

This paper introduces a nonlinear curve fitting method to determine the reverberation chamber time constant more efficiently, reducing measurement time and improving robustness for various applications including antenna testing and medical exposure assessments.

Contribution

A novel nonlinear curve fitting technique that reduces measurement samples by 60%, enabling faster and more robust determination of the chamber time constant.

Findings

Achieves 60% fewer samples for the same uncertainty.

Demonstrates accuracy through absorption cross-section measurements.

Verifies uncertainty with Monte-Carlo simulations.

Abstract

Determination of the rate of energy loss in a reverberation chamber is fundamental to many different measurements such as absorption cross-section, antenna efficiency, radiated power, and shielding effectiveness. Determination of the energy decay time-constant in the time domain by linear fitting the power delay profile, rather than using the frequency domain quality-factor, has the advantage of being independent of the radiation efficiency of antennas used in the measurement. However, determination of chamber time constant by linear regression suffers from several practical problems, including a requirement for long measurement times. Here we present a new nonlinear curve fitting technique that can extract the time-constant with typically 60% fewer samples of the chamber transfer function for the same measurement uncertainty, which enables faster measurement of chamber time constant by sampling fewer chamber transfer function, and allows for more robust automated data post-processing. Nonlinear curve fitting could have economic benefits for test-houses, and also enables accurate broadband measurements on humans in about ten minutes for microwave exposure and medical applications. The accuracy of the nonlinear method is demonstrated by measuring the absorption cross-section of several test objects of known properties. The measurement uncertainty of the method is verified using Monte-Carlo methods.