There are several approaches that can be used to generate synthetic radar data for use in AI/ML applications. Building Confidence: Validating Synthetic Data Let’s take a closer look at how Ansys’ solution tackles these challenges. Still, to encourage widespread adoption of synthetic data, we need to build confidence in artificially generated data, validate it, and prove we can acquire it at scale with accuracy. This radar sensor simulation capability is available within Ansys AVxcelerate Sensors add-ons and provides the core toolset needed to generate high-quality synthetic data for radar applications. This solver is based on the same SBR solver found within Ansys HFSS and has been graphics processing unit (GPU)-accelerated to perform simulation in real time. To remedy these concerns, Ansys has developed a simulation workflow that enables you to model complex radar scenarios in real time using an electromagnetic (EM) simulation technique based on the shooting and bouncing rays (SBR) method. Coupled with that, radar systems are complex and require high-quality data. However, there is a concern that synthetic data may not accurately represent real-world systems and phenomena. It can be a valuable tool for training ML models with its ability to overcome data scarcity issues, simulate rare or hard-to-observe scenarios, and augment real-world data. To recap, synthetic data is information that is artificially generated rather than collected from real-world observations. In our previous blog in this series, we learned what synthetic data is and its importance in training artificial intelligence/machine learning (AI/ML) systems for radar applications.
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