© April 2001
Outline of the algorithm
L5 Ping 49 Klick to enlarge Figure 1: Full-waveform data from a Reson SeaBat Ping. Beam number zero is nadir beam. Times are in seconds x 10. Klick to enlarge Figure 2: Beam footprints of a survey line. Light-grey dots mark available beams, dark-grey dots mark beams of satisfactory S/N. Depth contours are generated from travel-times of dark-grey dotted beams. To provide meaningful input to a classification algorithm, detection becomes a task of isolating and selecting returns which have sufficiently high signal to noise ratios. This is particularly important for the generation of training-sets, since noise-dominated signals will invariably bias the associative memories. Detection of a return can be viewed as a two step process. The noise level is estimated on a sample by sample basis, from the begin of the recording. Whenever a sample amplitude exceeds the adaptive noise threshold, a parametric spectrum at that particular instant in time is estimated. If sufficient power is present in the expected frequency band, the onset of a signal return is assumed. Relatively good control over signal quality can be exercised by introducing a factor > 1 by which the signal amplitude has to be above the current noise level in order to qualify for a detection. Figure 2 shows a multi-beam profile with light-grey dots marking the footprints of available beams. The dark-grey dots show beams which were actually detected as "suitable" for further processing and it is no surprise that mostly wide-angle beams have been discarded.
Abstraction
Klick to enlarge Figure 3: Time-Frequency representation of a sonar return
Since relatively little is known about the statistics of backscattering, this approach is simply not feasible for multi-beam sonar data. In fact, the proposed method seeks to increase the data variance, trusting, that the associative memories will lock on to the most salient views of the data. The capability of associative memories to generate and retain salient views has been very well demonstrated with object recognition applications.
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