Development of a Human Performance Baseline of Lay Error in Targeting
Abstract
In fire control, a primary source of error directly contributed by a gunner is called the lay error--the gunners inability to lay the sight crosshairs exactly on the center of the target. As the U.S. Army considers the development of computer vision, artificial intelligence algorithms, and associated systems to assist direct-fire gunners target performance, it is essential to establish a baseline of human performance against which to compare such new systems. In this work, we were motivated by two objectives: 1) Develop a model to represent a humans ability to lay the sight crosshairs on the center of the target. 2) Study the influence of engagement condition parameters (e.g., shape, size, range, motion) on the observed lay error and determine if single error measurement is sufficient to be extrapolated across any target. To address these objectives, a photorealistic simulation environment using the Unreal Engine was designed and developed, featuring a variety of targets and shooting conditions. The simulation environment (final prototype) includes four different targets, four motion configurations, five levels of zoom, and four ranges. Following a series of prototyping, testing, and evaluation, our simulation environment was used for collection of lay error, firing time, and human subjects feedback (post-study analysis). Following an Institutional Review Board-approved protocol, 15 college-student subjects used our simulation environment and were instructed to align crosshairs on targets at multiple ranges under various motion conditions. Each participant aimed and fired 240 times for a total of 3600 shots over the course of the study. After data collection, we conducted various statistical analyses of lay error.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 25, 2024
- Accession Number
- AD1219702
Entities
People
- Cody Lundberg
- Jennifer Forsythe
- Nicholas Gans
- Parker Ensing
- Thirimachos Bourlai
Organizations
- University of Georgia
- University of Texas at Arlington