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

Abstract

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.