Modeling Space-Based Intelligence, Surveillance, and Reconnaissance (ISR) in Combat Simulations

An analysis of the development of methodologies for representing the performance of commercial, national, and military space and low-earth-orbit assets and their impact on joint operations with a test implementation within the Framework for Capability-based Tactical Analysis Libraries and Simulations (FRACTALS).

During the 2021 Modeling and Simulation (M&S) Gap Forum, space intelligence, surveillance, and reconnaissance (ISR) modeling was identified as a current/near-future modeling gap. The U.S. Army Combat Capabilities Development Command (DEVCOM) Analysis Center (DAC) submitted an Army M&S Enterprise Capability Gap white paper (Harclerode, 2021) describing a course of action to help fill this gap. The Army Modeling and Simulation Office has funded DAC to develop methodologies for representing performance of commercial, national, and military space and low-earth- orbit assets and their impact on joint operations with a test implementation within the Framework for Capability-based Tactical Analysis Libraries and Simulations (FRACTALS).

FRACTALS is a DAC-developed simulation framework that provides the generic architectural “building blocks” to model, simulate, and assess performance of ISR systems in tactical-level missions and tasks. FRACTALS serves as a testbed for the various ISR performance methodologies developed at DAC to be incorporated into Force on Force simulations via methodology documentation and/or data. FRACTALS also serves as an analytical tool within DAC to execute performance analysis comparisons of ISR systems in tactical settings.

This effort requires some level of representation of satellite vehicles (altitude, trajectory, and kinematics), sensor payloads (electro-optical [EO], IR, synthetic aperture radar, and signals intelligence), networks, control systems, ground stations (timeline, communications, processing, exploitation, and dissemination), end users, and the processes and behaviors that connect them. The research describes some of the groundwork that DAC has performed to support this effort with a focus on visible-band camera imagery.

Figure 1. Example of TLE data.

FRACTALS represents the orbital location of satellites through use of the Simplified General Perturbations 4 (SGP4) algorithm and the Two-Line Element (TLE) Set. The TLE Set contains the orbital characteristics of a satellite at a single moment in time, and the SGP4 algorithm will extrapolate the satellite's orbital location at a specific time in the future using the TLE set. Errors in orbital location will increase as the length of time between the TLE set and the extrapolation increases.

Up-to-date TLE sets for commercial satellites such as the one shown in Figure 1 can be obtained from a variety of sources. With the SGP4 algorithm and TLE sets, FRACTALS can predict the timing, duration, and resolution for satellite coverage of an area of interest on the ground.

Figure 2. Pléiades 1A and 1B combined corridor of visibility for the same day (±30°).

Coverage and revisit rate (how much of the earth you can see and how often) are two defining features of satellite constellations. Figure 2, extracted from the Pléiades Imagery User Guide (ASTRIUM, 2012), shows the global coverage available from two satellites. Using both satellites with view angles within ±30°, the revisit rate is 1.3 days or better above 40° latitude and 1.7 days at the equator. With view angles within ±45°, daily revisits are possible. A nadir view has a view angle of 0° and is looking straight down.

This work was done by John Mazz for the DEVCOM Analysis Center. For more information, download the Technical Support Package (free white paper)from the link below.


This Brief includes a Technical Support Package (TSP).
Modeling Space-Based Intelligence, Surveillance, and Reconnaissance (ISR) in Combat Simulations

(reference ARL-0247) is currently available for download from the TSP library.

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