C. E. Ruckman
Acoust. Technol. Dept., David Taylor Model Basin, Bethesda, MD 20084-5000
Chris R. Fuller
Virginia Polytech. Inst. and State Univ., Blacksburg, VA 24061
Current numerical techniques for simulating feedforward active noise control in the frequency domain are mathematically equivalent to the techniques required for linear least-squares regression. Measurement error in the control system error sensors plays a role analogous to that of observation error in a statistical regression. In the statistics literature, regressions are always accompanied by regression diagnostics, i.e., computed statistics that help assess the impact of observation error. However, discussions in the acoustics literature typically fail to address this issue. The present workmodels feedforward active control as a regression of complex-valued variables, and shows how to apply two basic regression diagnostics: the F-test and the t-test. Also discussed are the physical significant of the ``cost function'' being minimized by the regression, and the assumptions that must be made regarding the variance of the measurement error. The techniques are demonstrated by numerically simulating a simple system in which radiation from an axisymmetric cylindrical shell is controlled by oscillating forces applied on the shell surface. [Work supported by David Taylor Model Basin and ONR.]