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We Hear Your PACE: Passive Acoustic Localization of Multiple Walking Persons

Published: 24 June 2021 Publication History

Abstract

Indoor localization is crucial to enable context-aware applications, but existing solutions mostly require a user to carry a device, so as to actively sense location-discriminating signals. However, many applications do not prefer user involvement due to, e.g., the cumbersome of carrying a device. Therefore, solutions that track user locations passively can be desirable, yet lack of active user involvement has made passive indoor localization very challenging even for a single person. To this end, we propose Passive Acoustic loCalization of multiple walking pErsons (PACE) as a solution for small-scale indoor scenarios: it passively locates users by pinpointing the positions of their footsteps. In particular, PACE leverages both structure-borne and air-borne footstep impact sounds (FIS); it uses structure-borne FIS for range estimations exploiting their acoustic dispersion nature, and it employs air-borne FIS for Angle-of-Arrival (AoA) estimations and person identifications. To combat the low-SNR nature of FIS, PACE innovatively employs domain adversarial adaptation and spectral weighting to ranging/identification and AoA estimations, respectively. We implement a PACE prototype and extensively evaluate its performance in representative environments. The results demonstrate a promising sub-meter localization accuracy with a median error of 30 cm.

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Supplemental movie, appendix, image and software files for, We Hear Your PACE: Passive Acoustic Localization of Multiple Walking Persons

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    cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 5, Issue 2
    June 2021
    932 pages
    EISSN:2474-9567
    DOI:10.1145/3472726
    Issue’s Table of Contents
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    Publication History

    Published: 24 June 2021
    Published in IMWUT Volume 5, Issue 2

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    Author Tags

    1. Passive indoor localization
    2. acoustic sensing
    3. angle-of-arrival
    4. domain adversarial adaptation
    5. ranging
    6. user identification

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    • (2024)ASLiquid: Non-Intrusive Liquid Counterfeit Identification with Your EarphonesProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699321(41-53)Online publication date: 4-Nov-2024
    • (2024)RefleXnoop: Passwords Snooping on NLoS Laptops Leveraging Screen-Induced Sound ReflectionProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3670341(3361-3375)Online publication date: 2-Dec-2024
    • (2024)FusionTrack: Towards Accurate Device-free Acoustic Motion Tracking with Signal FusionACM Transactions on Sensor Networks10.1145/365466620:3(1-30)Online publication date: 6-May-2024
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    • (2024)Indoor Multiperson Detection and Recognition Through Footsteps: A Deep Learning Approach With Acoustic Signal AnalysisIEEE Sensors Journal10.1109/JSEN.2024.339421224:12(19482-19496)Online publication date: 15-Jun-2024
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