Proceedings Article | 2 May 2017
John Hodge, Kerron Duncan, Madeline Zimmerman, Rob Drupp, Mike Manno, Donald Barrett, Amelia Smith
KEYWORDS: Systems modeling, Performance modeling, Radar, Integrated modeling, Thermal modeling, Optimization (mathematics), Antennas, Data modeling, Instrument modeling, Visualization, Model-based design, Systems engineering, Modeling and simulation
A fully integrated Mission-Level Radar model is in development as part of a multi-year effort under the Northrop
Grumman Mission Systems (NGMS) sector’s Model Based Engineering (MBE) initiative to digitally interconnect and
unify previously separate performance and cost models. In 2016, an NGMS internal research and development (IR and D)
funded multidisciplinary team integrated radio frequency (RF), power, control, size, weight, thermal, and cost models
together using a commercial-off-the-shelf software, ModelCenter, for an Active Electronically Scanned Array (AESA)
radar system. Each represented model was digitally connected with standard interfaces and unified to allow end-to-end
mission system optimization and trade studies. The radar model was then linked to the Air Force’s own mission
modeling framework (AFSIM).
The team first had to identify the necessary models, and with the aid of subject matter experts (SMEs) understand and
document the inputs, outputs, and behaviors of the component models. This agile development process and collaboration
enabled rapid integration of disparate models and the validation of their combined system performance. This MBE
framework will allow NGMS to design systems more efficiently and affordably, optimize architectures, and provide
increased value to the customer. The model integrates detailed component models that validate cost and performance at
the physics level with high-level models that provide visualization of a platform mission. This connectivity of
component to mission models allows hardware and software design solutions to be better optimized to meet mission
needs, creating cost-optimal solutions for the customer, while reducing design cycle time through risk mitigation and
early validation of design decisions.
