Smartphones chirping: A collaborative acoustic positioning system using unsynchronized tones

Achieving accurate acoustic-based indoor positioning without synchronized clocks presents a significant challenge, particularly in collaborative systems where multiple devices must estimate pairwise distances simultaneously. In this paper, we propose a novel synchronization-free collaborative acoustic positioning system using standard smartphone speakers and microphones. Our system leverages a convolutional neural network (CNN) trained on Mel-frequency cepstral coefficient (MFCC) features for accurate pairwise distance estimation, followed by a lateration algorithm for position calculation. We demonstrate the feasibility of our approach through real-world experiments, achieving sub-meter positioning accuracy in the best-performing configuration (RMSE = 0.50 m, 90th percentile = 0.80 m). Other configurations, tested under more challenging conditions (e.g., long distances and less optimal geometric placements), exhibit higher errors. Our results highlight that low-frequency tones (440 Hz and 1000 Hz) provide more robust performance, while higher frequencies excel in mid-range distances but at near and far distances are more susceptible to environmental factors decreasing their reliability. Overall, the results indicate that our approach offers a promising solution for practical, synchronization-free acoustic positioning in collaborative systems.