Boosting the Sensing Granularity of Acoustic Signals by Exploiting Hardware Non-linearit

Abstract

Acoustic sensing is a new sensing modality that senses the contexts of human targets and our surroundings using acoustic signals. It becomes a hot topic in both academia and industry owing to its finer sensing granularity and the wide availability of microphone and speaker on commodity devices. While prior studies focused on addressing well-known challenges such as increasing the limited sensing range and enabling multi-target sensing, we propose a novel scheme to leverage the non-linearity distortion of microphones to further boost the sensing granularity. Specifically, we observe the existence of the non-linear signal generated by the direct path signal and target reflection signal. We mathematically show that the non-linear chirp signal amplifies the phase variations and this property can be utilized to improve the granularity of acoustic sensing. Experiment results show that, by properly leveraging the hardware non-linearity, the amplitude estimation error for sub-millimeter-level vibration can be reduced from 0.137 mm to 0.029 mm.