Reconstruction of Periodic Sonar Signals Hidden in Wideband Noise Using Ensemble Averaging and Multi-Rate DSP.

摘要

The reconstruction of periodic acoustical signals with time domain periodic averaging requires a reliable estimate of the fundamental frequency (fl) of the signal. The reconstruction task is particularly difficult when the signal is 'hidden' in additive noise and the signal-to-noise ratio is poor. This is usually the case in most passive SONAR problems when early detection and characterization of targets is required. Statistically reliable estimates of the fundamental frequency of a noisy periodic signal can be computed in the frequency domain using Bartlett's smoothing procedure. In this procedure, a long, noisy signal is segmented into M mutually exclusive time segments and a power spectral estimate for each segment is computed. Spectral estimates are ensemble-averaged to enhance the signal power and reduce the residual spectral variance of the additive noise. In Bartlett's smoothing procedure the spectral line detection efficiency improves with the square root of M when M is greater than 50. The Bartlett's smoothing procedure merely provides a range of values for the fundamental frequency within a range of four times the standard deviation of the embedded periodic signal. In the reconstruction phase, the recorded noisy signal is reused to obtain one or more cycles of the 'clean' signal. In the reconstruction procedure, the noisy signal is segmented into J mutually exclusive time segments, each exactly T seconds in length. Ensemble averaging in the time domain of these segments recovers the required 'clean' signal with an enhancement efficiency of J when N is greater than 50 and when the proper value of T is used. Because in most problems the correct value of T is not known, the enhancement procedure is iterated over a range of four times the standard deviation and that iteration which provides the maximum signal-to-noise ratio is declared the winner.