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We investigate the pre-processing of sonar signals prior to
using neural networks for robust differentiation of commonly
encountered features in indoor environments. Amplitude and time-of-flight measurement patterns acquired from a real sonar system are pre-processed using various techniques including wavelet transforms, Fourier and fractional Fourier transforms, and Kohonen's self-organizing feature map. Modular and non-modular neural network structures trained with the back-propagation and generating-shrinking algorithms are used to incorporate learning in the identification of parameter relations for target primitives. Networks trained with the generating-shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results
in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications. Neural networks can differentiate more targets, employing only a single sensor node,
with a higher correct differentiation percentage than achieved with previously reported methods employing multiple sensor nodes.
The success of the neural network approach shows that the sonar signals do contain sufficient information to differentiate a considerable number of target types, but the previously reported methods are unable to resolve this identifying information.
This work can find application in areas where recognition of patterns
hidden in sonar signals is required. Some examples are system control
based on acoustic signal detection and identification, map building,
navigation, obstacle avoidance, and target-tracking applications
for mobile robots and other intelligent systems.
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