Noise-source identification using microphone arrays has become well-established in wind-tunnel testing. When the sources are moving, the accepted approach is first to de-Dopplerize' the microphone signals, producing estimated source signals for subsequent analysis. A straightforward time-domain average of these signals is vulnerable to flow-noise contamination, so an alternative approach is necessary for wind-tunnel applications. This method is equivalent to the basic, diagonal-removal', cross-spectral technique employed with fixed sources, but, unlike the fixed-source case, cannot be generalized to account simultaneously for multiple sources. The alternative presented here, which works with the time-domain cross-correlations between microphones, is not subject to this limitation. It is illustrated using simulated data from an example rotating-source configuration, and the results are compared against a de-Dopplerization-based analysis. For three equal-strength sources, the current method eliminates the contamination evident in de-Dopplerization estimates. Equally, it is able to locate and accurately characterize weak secondary sources that are masked by primary-source contributions in de-Dopplerization beamforming maps. Hence it extends the option of higher fidelity wind-tunnel beamforming, currently only available for fixed sources, to rotating-source configurations.