Estimating Doppler Shift Using Bat-Inspired Cochlear Filter Bank Models: A Comparison of Methods for Echoes from Single and Multiple Reflectors

Abstract

This work presents biologically inspired engineering on the use of narrowband sonar in mobile robotics. It replicates, using robotics as a modeling medium, elements of how CF-FM bats process the constant frequency part of their emitted call for estimating Doppler shift in the echoes. The experimental platform for the work is RoBat, a 6-DOF biomimetic sonar head mounted on a 3-DOF mobile platform. Two models of the signal processing performed by the bat's cochlea were implemented, based on sets of bandpass filters followed by full-wave rectification and lowpass filtering. One filter bank uses Butterworth filters whose center frequencies vary linearly across the set. The alternative filter bank uses gammatone filters, with center frequencies varying nonlinearly across the set. Two methods of estimating Doppler shift from the return echoes after cochlear signal processing were implemented. The first was a simple energy-weighted average of filter center frequencies. The second was a novel neural-network-based technique. Each method was tested with each of the cochlear models and evaluated in the context of several dynamic tasks in which RoBat was moved at different velocities toward stationary echo sources such as walls and posts. Overall, the performance of the linear filter bank was more consistent than the gammatone. The same applies to the artificial neural network, with consistently better noise performance than the weighted average. The effect of multiple reflectors contained in a single echo was also analyzed in terms of error in Doppler-shift estimation assuming a single wider reflector.

Keywords

sonar bat robot Doppler filter bank gammatone ANN

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