Model-Based Systems Approach to Radar Design

This research was the result of a senior design project in 2017-2018 with two other Rose-Hulman colleagues in collaboration with Northrop Grumman. The customer, Northrop Grumman, tasked our senior design team with a Systems Engineering project to create a model-based system to radar design via an automated design approach. Through this project, the team created a system programmed in Python utilizing Qt as the front-end graphical user interface to provide an automated tradespace analysis tool to create radar designs.

Parts of this codebase is still in use at Northrop Grumman today, and the team went a step beyond the senior design objectives to improve their research. A professor from the systems engineering department saw the amount of work the team put to create this product for the customer and invited the team to write a research paper to be submitted at the 2018 Systems and Information Engineering Design Symposium (SIEDS) based on the work they had done. The team completed their research paper, presented the paper and project at SIEDS, and after review the paper was published in the IEEE Xplore database.

“A model-based systems approach to radar design utilizing multi-attribute decision analysis techniques”

This paper proposes an innovative system software solution for developing ground-based phased array radar architectures. Specifically, the paper looks at developing and integrating initial functional and analytical component and sub-system models into a system model that can be used for trade space exploration and optimization. This system software solution is implemented through a Python based suite of models that encapsulates the features and functions of standard phased array radars. Initial software design models create an integrated system model using classic systems engineering modeling and simulation techniques, which in turn allows a systems engineer to evaluate system level trade-offs and optimization for key radar performance parameters like signal-to-noise ratio, effective radiated power, and effective aperture size. By combining these system design models while using decision making algorithms such as non-dominated Pareto optimization and multi-attribute decision analysis, this tool optimizes different radar architectures. It then orders them by rank based on implicitly and explicitly derived system requirements and user-inputted preferences. Preliminary radar system design tests of the software suite show that the tool excels at taking system level technical requirements and breaking them down into functional and analytical models. This allows a trade space to be created containing all possible radar architectures, giving the systems engineer the ability to evaluate the potential architectures and view the optimization ranks given to them.