MOBILITY ENGINEERING Produced by SAE Media Group
MOBILITY ENGINEERING
Magazines
Magazine cover

Current Issue

Archives

Magazine cover

Current Issue

Archives

Magazine cover

Current Issue

Archives

Magazine cover

Current Issue

Archives

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.

Topics:
Aerospace Data Acquisition Data acquisition and handling Defense Defense industry Imaging Military vehicles and equipment Satellite communications Simulation and modeling Surveillance Surveillance and Security Telecommunications Vehicles
This Brief includes a Technical Support Package (TSP).
Document cover
Modeling Space-Based Intelligence, Surveillance, and Reconnaissance (ISR) in Combat Simulations

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

Don't have an account?

More From SAE Media Group

    Magazine cover
    Aerospace & Defense Technology Magazine

    This article first appeared in the June, 2022 issue of Aerospace & Defense Technology Magazine (Vol. 7 No. 4).

    Read more articles from this issue here.

    Read more articles from the archives here.

    Overview

    The technical report titled "Modeling Space-Based Intelligence, Surveillance, and Reconnaissance (ISR) in Combat Simulations," authored by John Mazz and published by the DEVCOM Analysis Center, focuses on developing methodologies to represent the performance of various space-based ISR assets in military combat simulations. The report is part of an effort to enhance the Army's capabilities in Multi-Domain Operations by integrating satellite imagery and data into combat scenarios.

    The report outlines the use of the Framework for Capability-based Tactical Analysis Libraries and Simulations (FRACTALS) as a test-bed for methodology development. It emphasizes the importance of commercial, national, and military satellite constellations that provide high-resolution imagery, specifically targeting resolutions of 1 meter or better. The methodologies discussed include the Simplified General Perturbations 4 (SGP4) algorithm, which is utilized to predict the orbital paths of satellites, thereby allowing for accurate modeling of satellite movements and their implications for ISR operations.

    Key components of the methodology include satellite positioning, global coverage, collection methods, and the impact of factors such as sensor characteristics, slant range, and viewing angles on image resolution. The report also addresses the National Imagery Interpretability Rating Scale (NIIRS) and Ground Sample Distance (GSD) as metrics for evaluating image quality and interpretability.

    Additionally, the report discusses the challenges posed by partial cloud cover on satellite imagery and presents methods for representing these effects in simulations. By incorporating these methodologies, the report aims to improve the fidelity of combat simulations, enabling military planners and operators to better understand the contributions of space-based ISR assets in joint operations.

    In conclusion, the report serves as a foundational document for future research and development in the field of military simulations, highlighting the critical role of satellite imagery in enhancing situational awareness and decision-making in combat scenarios. The findings are intended to support the Army's ongoing efforts to leverage advanced technologies for improved operational effectiveness in multi-domain environments.

    Top Stories

    INSIDERRF & Microwave Electronics

    Feature Image FAA to Replace Aging Network of Ground-Based Radars

    PodcastsDefense

    Feature Image A New Additive Manufacturing Accelerator for the U.S. Navy in Guam

    NewsSoftware

    Feature Image Rewriting the Engineer’s Playbook: What OEMs Must Do to Spin the AI Flywheel

    Road ReadyPower

    Feature Image 2026 Toyota RAV4 Review: All Hybrid, All the Time

    INSIDERDefense

    Feature Image F-22 Pilot Controls Drone With Tablet

    INSIDERRF & Microwave Electronics

    Feature Image L3Harris Starts Low Rate Production Of New F-16 Viper Shield

    Webcasts