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This paper describes the integration of wavelet analysis and time -domain beamforming of microphone array output signals for analyzing the acoustic emissions from airplane generated wake vortices. This integrated process provides visual and quantitative simultaneous information about the wake signal composition and array resolution for a particular wavelet subspace during a time interval , T. In the results section, an exam ple is given on how image processing algorithms might be used to automate the extraction of this information and select the wavelet subspaces from which to perform image reconstruction. This process begins with the projection of all the microphone signals on wavelet multi - resolution subspaces. The projections of these signals on the same wavelet subspace or scale are then beamformed to produce an image of the wake corresponding to that particular scale . Therefore for each time interval T, the process produc es a number of images equal to that of the wavelet s cales . This is equivalent to a more conventional Fourier -based idea of filtering the microphone signals with band -pass filters having non -uniform bandwidths then beamform in different sub -bands, but offer s greater flexibility and enhanced computational speed. Results from both approaches will be shown, which ultimately illustrate the advantage s of wavelet analysis over that of the Fourier -based analysis . Amongst the advantages are the speed of the decompos ition and ease of the image reconstruction from selected subspaces aided by the perfect reconstruction and orthogonality properties of wavelet analysis .

Wake Acoustic Analysis and Image Decomposition via Beamforming of Microphone Signal Projections on Wavelet Subspaces