Soldier State Estimation: Modelling and Analysis (Summary Technical Report, Oct 2020-Sep 2021)
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 units 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 2022
- Accession Number
- AD1167298
Entities
People
- Mark Dennison
- Michael H. Lee
Organizations
- United States Army