ACE: An ATAK Plugin for Enhanced Acoustic Situational Awareness at the Edge

Superior battlefield Situational Awareness (SA) requires timely and coherent integration of various sensor modalities to provide the most complete, real-time picture of in-theater activities. In this work, we introduce Acoustic Classification at the Edge (ACE), an ATAK plugin for improved acoustic SA, to move beyond traditional full-motion video and geospatial data typically employed for SA, and instead focus on acoustic intelligence. Our Android Tactical Awareness Kit (ATAK) plugin is able to perform on-device audio recording, classification, labeling, and autonomous reach-back to the cloud, when available, to enable warfighters to improve SA over time. As part of ACE, we detail a machine learning analytic designed to classify acoustic sources directly at the edge, with a case study on firearm classification. We also detail the cloud infrastructure necessary to support it. This paper describes the application and cloud architecture, in-theater operations, and experimental results after having deployed the plugin on ATAK. Finally, we propose future directions for acoustic classification at the edge based on our findings.