Sonar sensing algorithm inspired by the auditory system of big brown bats by Chen Ming
Channel: AI Institute at UofSC - #AIISC
Date: 2021-03-26

Echolocating animals rely on biosonar for navigation and foraging, operating on lower energy input but achieving higher accuracy compared with engineered sonar. My research focuses on understanding the mechanism of bat biosonar by simulating different acoustic scenes involving vegetation and defining the invariants in the foliage echoes that provide the tree type information for bats to use as landmarks. Additionally, I have developed Spectrogram Correlation and Transformation (SCAT) model that simulates the bat’s auditory system with a gammatone filterbank, a half-wave rectifier, and a low-pass filter. The SCAT model splits a signal into many parallel frequency channels and maps the acoustic “image” of the target by finding the crossings at each channel with the same threshold. It can estimate the range delay between a sound source and targets as well as fine delay within reflecting points in one target – signal delays as short as a few microseconds. Currently, I am expanding the SCAT model by including a convolutional neural network for binaural localization of small targets.