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Accurate imaging of target shape is a crucial aspect of wideband FM biosonarin echolocating bats, for which we have developed new algorithms that provide asolution for the shape of complicated targets in the computational domain. Weuse recurrent neural networks and convolutional neural networks to determinethe number of glints (i.e., major reflecting surfaces) making up the target'sstructure and the distances between the glints (target shape in sonar). Echoesare dechirped relative to broadcasts, and the dechirped spectrograms arescanned in short time segments to find local spectral ripple patterns arisingfrom different interglint delay separations. By proceeding in successivetime-window slices, we mimic time-frequency neural processing in the bat'sauditory system as a novel means of real-time target discrimination for sonarsensing in robotics.

Target Geometry Estimation Using Deep Neural Networks in Sonar Sensing