Soldier State Estimation: Modelling and Analysis (Summary Technical Report, Oct 2020–Sep 2021)

Author(s):

Mark Dennison and Michael H Lee

Abstract:

Enabling leaders with the ability to make decisive actions in high-operational tempo environments is key to achieving decision-superiority. Under stressful battlefield conditions with little-to-no time for communication, it is critical to acquire relevant tactical information quickly to inform decision-making. A potential augmentation to tactical information systems is access to real-time analytics on a unit’s operating status and emergent behaviors inferred from Soldier-worn or embedded sensors on their kit. Automatic human activity recognition (HAR) has been greatly achievable in recent years thanks to advancements in algorithms and ubiquitous low-cost—yet powerful—processors, hardware, and sensors. This work utilized weapon-born sensor measurement acquisition, processing, and HAR approaches to demonstrate Soldier state estimation in a target acquisition and tracking experiment. The Soldier states that were classified included whether the Soldier was at rest, tracking a target, transitioning between potential targets, or firing a shot at the target. We implemented Multivariate Time Series Classification using the “SKTime” toolkit for this work and discuss the performance from various classification methods. We also discuss a framework for efficient transference of this information to other tactical information systems on the network.