Proc. SPIE. 7348, Modeling and Simulation for Military Operations IV
KEYWORDS: Data modeling, Calibration, Databases, Computer simulations, Monte Carlo methods, Telecommunications, Analytical research, Modeling and simulation, Computer architecture, Standards development
This paper describes the application of a new parallel and distributed modeling and simulation technology known as
HyperWarpSpeed to facilitate the decision-making process in a time-critical simulated Command and Control
environment. HyperWarpSpeed enables the exploration of multiple decision branches at key decision points within a
single simulation execution. Whereas the traditional Monte Carlo approach re-computes the majority of calculations for
each run, HyperWarpSpeed shares computations between the parallel behaviors resulting in run times that are potentially
orders of magnitude faster.
For Air Operations Centers, there is a need to provide Commanders and their staff with real-time, up-to-the-second
information regarding Red-Force, Blue-Force, and neutral force status and positioning. These updates of the real-time
picture provide Command Staff with dynamic situational awareness of their operations while considering current and
future Courses of Action (COAs). A key shortfall in current capability is that intelligence, surveillance, and
reconnaissance (ISR) sensors, electronic intelligence, and human intelligence only provide a snapshot of the operational
world from "observable" inputs. While useful, this information only provides a subset of the entire real-time picture. To
provide this "missing" information, techniques are required to estimate the state of Red, Blue, and neutral force assets
and resources. One such technique for providing this "state" information is to utilize operationally focused simulation to
estimate the unobservable data. RAM Laboratories and the Air Force Research Laboratory's Information Systems
Research Branch are developing a Dynamic Situation Assessment and Prediction (DSAP) Software Framework that, in
part, utilizes embedded real-time simulation in this manner.
This paper examines enhancements made to the DSAP infrastructure's Multiple Replication Framework (MRF) and
reviews extensions made to provide estimated state information via calibrated real-time simulation. This paper also
provides an overview of the Effectiveness Metrics that can be used to evaluate plan effectiveness with respect to the realtime
inputs, simulated plan, and user objectives.
RAM Laboratories and AFRL are developing a software infrastructure to provide a Dynamic Situation Assessment and
Prediction (DSAP) capability through the use of an embedded simulation infrastructure that can be linked to real-time
Command, Control, Communications, and Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) sensors
and systems and Command and Control (C2) activities. The resulting capabilities will allow Commanders to evaluate
and analyze Courses of Action and potential alternatives through real-time and faster-than-real-time simulation via
executing multiple plans simultaneously across a computing grid. In order to support users in a distributed C2
operational capacity, the DSAP infrastructure is being web-enabled to support net-centric services and common data
formats and specifications that will allow it to support users on the Global Information Grid. This paper reviews DSAP
and its underlying Multiple Replication Framework architecture and discusses steps that must be taken to play in a
Technological advances and emerging threats reduce the time between target detection and action to an order of a few minutes. To effectively assist with the decision-making process, C4I decision support tools must quickly and dynamically predict and assess alternative Courses Of Action (COAs) to assist Commanders in anticipating potential outcomes. These capabilities can be provided through the faster-than-real-time predictive simulation of plans that are continuously re-calibrating with the real-time picture. This capability allows decision-makers to assess the effects of re-tasking opportunities, providing the decision-maker with tremendous freedom to make time-critical, mid-course decisions.
This paper presents an overview and demonstrates the use of a software infrastructure that supports DSAP capabilities. These DSAP capabilities are demonstrated through the use of a Multi-Replication Framework that supports (1) predictivie simulations using JSAF (Joint Semi-Automated Forces); (2) real-time simulation, also using JSAF, as a state estimation mechanism; and, (3) real-time C4I data updates through TBMCS (Theater Battle Management Core Systems). This infrastructure allows multiple replications of a simulation to be executed simultaneously over a grid faster-than-real-time, calibrated with live data feeds. A cost evaluator mechanism analyzes potential outcomes and prunes simulations that diverge from the real-time picture. In particular, this paper primarily serves to walk a user through the process for using the Multi-Replication Framework providing an enhanced decision aid.
Recent technological advances and emerging threats greatly compress the timeline between target detection and action to an order of a few minutes. As such, decision support tools for today's C4I systems must assist commanders in anticipating potential outcomes by providing predictive assessments of alternate Courses Of Action (COAs). These assessments are supported by faster-than-real-time predictive simulations that analyze possible outcomes and re-calibrate with real-time sensor data or extracted knowledge in real-time. This capability is known as a Dynamic Situation Assessment and Prediction (DSAP) capability. This capability allows decision-makers to assess the effects of re-tasking opportunities, providing the decision-maker with tremendous latitude to make time-critical, mid-course decisions.
This paper details the development of a software infrastructure that supports a DSAP capability for decision aids as applied to a Joint Synthetic Battlespace for Research and Development (JSB-RD). This infrastructure supports capabilities that allow objects to be dynamically created, deleted and reconfigured, allows simulations to be calibrated with live data feeds, and provides a reduction in overheads for simulations in order to execute faster-than-real-time in order to provide a predictive capability. In particular, this paper will focus on a Multiple Replication Framework that can be used to support a DSAP infrastructure.