Radar Systems Meets Digital Engineering

I've posted on PSG's initiatives in radar systems engineering as well as our competency in Model-Based Systems Engineering (MBSE). We have recently taken on the noble cause of combining these two competencies into a digital radar processing systems engineering practice. In the process we have expanded skills in both dimensions and added additional competencies in software development needed to perform the complex data workflows of this exquisite science.

In recent years, the power of Artificial Intelligence/Machine Learning (AI/ML) has grown out of Computer Vision and entered the Radio Frequency domain including radar. Now the complex physics-based radar processing workflows are combined with non-linear neural networks to achieve new levels of performance in radar missions such as Automatic Target Recognition (ATR) and Moving Target Indication (MTI). The result is complexity squared to squeeze out higher levels of performance to meet increasing demands on system performance.

For example, we have investigated the concepts behind Cognitive Radar (CR). CR attempts to extrapolate what can be learned by a radar due to its coherent nature. This presents opportunities for a transmitter, receiver and waveform generator to collaboratively learn from sensing and adjust to new modes for better performance on time critical functions such as ATR and MTI. To understand all of this complexity, it is necessary to model the overall system and induce simulations that allow for physics-based signal processing to interact with learning neural networks.

Our strategy is to continue a push toward building skills in radar signal processing and adding AI/ML techniques in radar modes. This requires a systematic approach that is only possible through emerging digital engineering methods and tools. Radar systems engineers are truly gifted at systems thinking. The complexity of today's radar missions requires digital twins across the entire system to understand this complexity and turn it into useful capabilities that achieve more through systems engineering.