Acoustic-based 2-D target tracking with constrained intelligent edge device

Abstract

Edge Computing has shown significant potential in the development of IoT. However, most existing constrained intelligent edge devices cannot provide sufficient resources for edge computing. Such as the existing acoustic 2-D tracking approaches for portable devices require at least two microphones, incapable for universal devices. In this paper, we propose NearTracker+, a contactless acoustic tracking system for the interaction between users and IoT devices, achieves 2-D target tracking with low-cost hardware (i.e., one speaker and one microphone) and lightweight computation. With the help of a nearby reflector, the additional valuable echoes from target are combined for positioning. However, the dynamic interferences from non-target echoes pose huge challenges for target echo extraction. NearTracker+ extracts and enhances these faint target echoes with novel signal processing methods and estimates the target’s location accurately via a designed particle filter algorithm. All the above data processing is computed locally in a low-latency and high-privacy way. Extensive experiments show that our system achieves on average 1.36 cm error with edge computing for 2-D target tracking, which can satisfy universal edge devices and application scenarios.