Type of Research:
Military.
Summary:
The intended audience for this paper is Army officers who are attending graduate school (primarily the Simulation Masters program at the University of Central Florida) with the hope of directing their research towards topics that ar immediately applicable to specific needs of STRICOM (Simulation, Training and Instrumentation Command).
The 17 specific topics are:
Weapon & Ammunitions Prioritization: a system that automatically determines the write weapon and ammunition for the target rather than depending on human judgment.
Target Prioritization & Disengagement: “Given an array of targets in an intense battle, one of the most difficult issues a crew has is which target it should engage.”
User Control Interface (UCI)/Workstation & Execution Matrices Improvements: Not only how information is displayed but how low-level units interact and how information is shared between them.
Maneuver/Obstacle Avoidance/ Routing Improvement: “One of the most common complaints from SAF (Semi Automated Forces) operators is the maneuver capability of the SAF entities.”
Suppressive Fire Effects: “One weakness in current virtual simulations and CGFs is a realistic simulation of suppressive fire effects.”
Stability and Support Operations (SASO), Nuclear Biological Chemical (NBC), or Amphibious Operations Requirement Analysis: These new units types are not accurately modeled or included in CGF simulations.
Virtual Warrior/ Infantry Simulation: “Most of the simulators and CGFs were based on supporting tank simulation. As the virtual and CGF technology progressed it became apparent that infantry had to be added to the simulation. The addition of infantry has proven to be difficult because of it unique behavior and maneuver characteristics.”
Military Operations in Urban Terrain (MOUT): There are numerous problems with urban combat simulation including Line of Sight (LOS), 3D maneuvering, changing terrain (building damage), etc., etc.
Situational Behavior Improvements: The CGF AI needs a great deal of improvement.
Digital Message Processing: Creating the ability for the CGF to send and receive and respond to messages.
Fair Fight with Manned Simulators: Manned simulators and SAF are not “equal” in information.
Executive Framework Control: Optimization of ModSAF and CCTT SAF which has some definite time and size problems.
Demand-Driven Simulation: Optimization of training simulations so that they would not spend CPU time and resources calculating unnecessary data.
Scalability Analysis of Simulation Problems and Algorithms: “This analysis could characterize the potential for time-space tradeoffs in algorithms [e.g. Line-of-Sight determination, Route Planning] for solving these problems, determine the extent to which these problems can be solved via parallel or distributed algorithms, and describe the effects of bandwidth constraints and network latency on parallel or distributed solutions.”
Command from Simulator (CFS) Issues: In order to “support Command Force Exercises (CFX) whose focus is the training and exercise of commanders and leaders, an operational mode called CFS was created in CCTT SAF. In other SAF’S it has been refereed to as a tethered unit.” Apparently this kludge has caused numerous problems including units not stopping “on an intervisability line such as a ridgeline.”
Aggregate-Disaggregate Algorithms: “For example, the destruction of one tank in a platoon is not the same as minor damage to two of the tanks, but commonly used reaggregation techniques might map these back to the same constructive representation.”
Opposing Forces (OPFOR) Skill Levels/Variable Skill Leadership Behaviors: “Current SAF systems have methods to create different skill levels that modify detection, engagement rates and delivery accuracy based on the competency level identified. This implementation(s) are mainly speculative and need an empirical analysis based on actual field data.”