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The experience in utilizing a phased microphone array to passively image aircraft wake vortices is highlighted. It is demonstrated that the array can provide visualization of wake dynamics similar to smoke release or natural condensation of vortices. Examples on how the technique has been integrated with other data to address some of the research issues in wake vortices are documented. These topics include the initial vortex spacing characterization, collaboration with simultaneous pulsed Lidar data to provide a more complete understanding of vortices at a late stage in their evolution, and correlation between atmospheric turbulence with the time-scale involved for vortices developing into a very contorted state. In addition, the paper provides a comparison between the vortex tracks as inferred from their acoustics characteristics vs. those detected from the velocity field (i.e., pulsed Lidar). A preliminary attempt is also made to examine the global link between wake vortex sound and vortex circulation, suggesting that there could be a power law relationship between the two datasets.

is an electrical engineer with the U.S. Department of Transportation at the Volpe Center where he has worked on various aspect of aviation related activities including wake vortex research, feasibility assessment of a staffed virtual tower, and Flight Technical Error (FTE) characterization. He is currently the program lead on the GPS Adjacent Band interference project. He received his B.S. in Electrical engineering from the University of Massachusetts Amherst.

The ability to unambiguously identify the acoustic energy generated by airplane wake vortices using phased microphone-array intensity distribution maps is hampered by the lack of knowledge of the associated non-stationary spectrum throughout the wake’s duration. This ambiguity is especially pronounced when the low broadband signal to noise ratio renders the wake signal difficult to isolate from contaminating background noise. In this paper, a technique to isolate the acoustic spectral content of a cross sectional wake track (CSWT) is presented. First, image processing is used to generate a CSWT from vertical intensity distribution maps. In a second beamforming stage, the array’s main-lobe traces the track to amplify the acoustic signal along the CSWT and estimate its time-varying power spectral density PCSWT(f) using Short Time Fourier Transform. Simultaneously, the background noise frequency-dependent upper confidence limit CL(f) is estimated at each time increment by steering the array away from the wake source. The time varying CL(f) is then used as an adaptive threshold function on PCSWT(f) in order to dynamically segment out the wake frequency bands from those of the contaminating noise sources.