Using the Laser Marksmanship Training System to Predict Rifle Marksmanship Qualification
Author | : Monte D. Smith |
Publisher | : |
Total Pages | : 30 |
Release | : 2003 |
ISBN-10 | : UIUC:30112055142290 |
ISBN-13 | : |
Rating | : 4/5 (90 Downloads) |
Download or read book Using the Laser Marksmanship Training System to Predict Rifle Marksmanship Qualification written by Monte D. Smith and published by . This book was released on 2003 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: To determine the relation between simulation- (Laser Marksmanship Training System LMTS) and live-fire-based rifle marksmanship performance, 186 Reserve Component (RC) soldiers from Idaho and Oregon fired for qualification on a scaled LMTS version and live-fire version of the Army's standard pop-up target qualification course. LMTS was fired under either a dry-fire mode or a Blazer (i.e., sound/recoil replicator) mode. Statistically significant positive linear relations were found (and then validated) between first-run live-fire scores and both LMTS dry-fire- (r = .50) and Blazer-based (r = .55) scores. These relations were of sufficient strength to permit development of easy-to-use tools for accurately predicting soldier chances of first-run, live-fire qualification. With these tools, RC marksmanship trainers can implement a competency- based training program where soldiers most in need of remedial training (i.e., poor shooters) can be quickly identified, and the point at which sufficient training has been provided (i.e., when first-run live-fire qualification is likely) easily determined. These tools also provide RC unit commanders with empirically derived live-fire performance standards needed to support use of LMTS in place of live-fire for rifle marksmanship proficiency validation purposes when standard pop-up target course range facilities are not readily available. Although both tools will serve these purposes, that based on LMTS dry-fire is recommended because of the added expense of firing with Blazer without an accompanying statistically significant increased predictive benefit.