Modeling and Simulation
The first hurdle for a researcher looking to secure funding is to show that, conceptually, their idea is viable. It’s critical to explore the viability of a novel concept before spending time and money on testing it out with real-world hardware—and that’s why the modeling and simulation phase is so important.
Radar researchers and systems engineers often use tools such as MathWorks MATLAB software and MathWorks Simulink® software to interactively design waveforms and sensor arrays and to explore various complex trade-offs across the entire multifunction radar system workflow—from RF and antenna components to signal processing algorithms. Because you can design and debug complete radar models at the earliest stage of the project, you can preempt and eliminate costly redesigns and utilize automatically generated code within tactical systems.
For accelerated development, MathWorks offers libraries of algorithms—including matched filtering, adaptive beamforming, target detection, space-time adaptive processing, and environmental and clutter modeling—that you can customize for your specific application. Additionally, you can model ground-based, airborne, and ship-borne radar platform scenarios, as well as moving targets across many domains.
Consider a scenario in which you need to upgrade an existing radar system to increase the maximum unambiguous range, detect targets with rapidly varying radar cross sections (RCS), and avoid interference with newly deployed 5G networks. Let’s say that the existing radar system uses a pulsed waveform, with relatively low transmission power and high pulse repetition frequency (PRF). It is logical that increasing the pulse interval (therefore, reducing PRF) will help to meet the increased maximum range requirement. Also, one way to detect targets with fluctuating RCS would be to boost the signal-to-noise ratio (SNR) by transmitting at higher power. One of these changes likely is trivial (a software parameter update to reduce the waveform’s PRF) but increasing transmission power may require significant and expensive hardware updates. Experimenting within a simulated environment helps you evaluate this design space— either increasing confidence or sparking a pivot to explore alternatives—before implementing such costly changes. For example, an alternative approach might replace the basic pulsed waveform with a linear FM waveform to use lower peak transmit power. For interference avoidance, you’d need to develop new algorithms that sense the RF spectrum, so that the radar would behave in a cognitive or adaptive manner, shifting its operating frequency to find the least-congested spectral areas.
To validate whether this approach works for you, you can simulate the radar model, its surrounding environment, targets, and other EM signals within MATLAB. To see whether the radar will detect targets with fluctuating RCS, create targets with a variety of Swerling models. You then can use MATLAB and Simulink to explore whether the radar system modifications are sufficient, demonstrate their impact on the rest of the system, and deploy to software defined radio (SDR) hardware for real-world testing and implementation.