Computational model of a bioinspired broadband receiver for sonar clutter reduction.

Abstract

Echolocating bats have the ability to seamlessly navigate through dense foliage and other obstacles at flight velocity using only the information available in acoustic returns. The spatial resolution required to perform this feature cannot be explained by conventional beamforming and pulse design techniques. We describe a biologically inspired broadband sonar receiver that mimics parallel neural processing by echolocating bats to suppress clutter in complex acoustic environments. These results are incorporated into an improved version of the spectrogram correlation and transformation (SCAT) receiver by replacing the original spectrogram transformation block with a process that translates both harmonic coherence and spectral interference patterns into estimates of echo‐delay separations for closely spaced echo highlights. The model treats simple target echoes as highlight reconstructions, and broader lowpass filtered echoes—characteristic of most off‐axis clutter—as numerous overlapping poorly defined shapes. The monaural SCAT model for range‐only resolution is also expanded to a binaural, 2‐D model for high‐resolution sonar imaging in the range‐azimuth plane. Receiver performance is compared with conventional array processing methods through acoustic simulation and sonar image reconstruction amidst dense clutter. [Work supported by the ONR and internal investments by the NUWC, Division Newport.]