AMT$^+$: Acoustic Multi-Target Tracking With Smartphone MIMO System

Acoustic target tracking has shown great advantages for device-free human-machine interaction over vision/RF-based mechanisms. However, existing approaches for portable devices solely track a single target, incapable of the ubiquitous and highly challenging multi-target situations such as double-hand multimedia controlling and multi-player gaming. In this paper, we propose
AMT
$^+$
, a pioneering smartphone MIMO system to achieve centimeter-level multi-target tracking. The challenge of multi-target occlusion is effectively addressed by employing multiple speaker-microphone pairs. However, the unique challenge raised by MIMO is the superposition of multi-source signals due to the cross-correlation among speakers. Initially, we tackle this challenge by designing a weak cross-correlation signal to reduce interference passively. In
AMT
$^+$
, we've further integrated self-interference cancellation for active minimize interference. The most distinguishing advantage of
AMT
$^+$
lies in the elimination of the raised multipath effect, which is commonly ignored in previous work by hastily assuming targets as particles.
AMT
$^+$
employs Doppler filtering over delay subtraction for echo suppression. Further, by non- particle target reflections modeling results, we introduce a distance-projection-based method for continuous target identification and tracking. Implemented on commercial smartphones,
AMT
$^+$
achieves on average 0.54 cm, 1.37 cm, and 2.13 cm errors for single, double, and triple target tracking respectively, and on average 97.0% classification accuracy for 14 controlling gestures.