Ray-Tracing Radar Simulation Without a 3D Environment

When people think about high-fidelity radar simulation, they typically imagine one thing: a detailed 3D environment.

Geometry, meshes, materials, everything needs to be modeled, prepared, and maintained before a single ray can be traced. But what if you could get the same physical fidelity without requiring a predefined 3D scene at all?

Ray tracing radar simulation without a predefined 3D environment

From object lists to physics-based radar simulation

Our radar model takes a different approach. Instead of relying on a pre-built environment, it uses ASAM OSI GroundTruth as its input. This input already contains a structured description of the scene, including road surfaces, guard rails, sign posts, and all moving objects such as vehicles and pedestrians.

Based on this information, the model reconstructs a 3D representation internally on the fly.

This means:

• no manual 3D setup is required

• no dependency on a specific simulation toolchain

• full compatibility with standard-compliant environments

In other words, the radar model operates directly on the same data that is already used to describe the scenario.

Ray tracing without compromise

Even though the model does not receive an explicit 3D environment, it is still a fully physical ray tracing model. From the object-based input, a virtual scene is generated and used by the radar engine to simulate signal propagation.

This enables the model to consider interactions with relevant surfaces such as asphalt or guard rails, while still remaining lightweight, real-time capable and easy to integrate.

Realistic interactions: surfaces and scattering centers

To accurately model radar reflections, different types of objects are treated differently.

Flat surfaces such as roads or guard rails are described using physical material properties, enabling realistic reflection behavior.

For moving objects like vehicles and pedestrians, we use scattering center models. These models approximate the object as a set of dominant reflection points, resulting in realistic radar cross section (RCS) profiles in the simulated data.

This combination allows the model to reproduce both:

• structured reflections from flat surfaces

• complex scattering behavior of dynamic objects

The result is a radar simulation that captures how objects actually appear to a sensor, not just geometrically, but in terms of signal strength and structure.

Capturing complex radar effects

This becomes particularly important when looking at real-world radar phenomena. Radar signals rarely take only the direct path. Instead, they are heavily influenced by multi-path propagation.

For example:

• signals can reflect horizontally along guard rails

• signals can bounce vertically via the ground and reach objects from “underneath” other vehicles

• multiple reflections can create ghost detections and unexpected signal patterns

Such effects are not edge case, they are part of everyday radar perception. Because our model performs ray tracing on the reconstructed environment, these interactions are explicitly simulated. Metal structures, ground surfaces, and object geometries all contribute to realistic signal propagation and reflections.

From rays to radar signals

The second key aspect of the model lies in how the radar signal itself is generated. Instead of stopping at reflection points or detection points, the model follows a Fourier tracing approach. This means the ray tracing results are directly translated into what a real radar sensor would output after signal processing. Each reflection contributes to a multi-dimensional radar cuboid, representing range, Doppler velocity, and angular dimensions.

This corresponds to the output of the sensor’s FFT processing, where time-domain signals are transformed into a frequency-domain representation.

As a result, the model does not just simulate detections, it simulates the full radar signal space, for example a range-Doppler map as in the image above.

From signal to detections

On top of this radar cuboid, standard signal processing steps are applied, including:

• CFAR

• peak detection

• interpolation

These steps transform the simulated signal into discrete radar detections, consistent with real sensor outputs.

Why this approach matters

The combination of:

• OSI-based inputs

• on-the-fly environment reconstruction

• physical ray tracing

• scattering-based object modeling

• signal-level radar simulation

creates a unique balance between flexibility and fidelity.

On the one hand, integration becomes significantly simpler as no dedicated 3D scene preparation is required. On the other hand, the model still captures the physical phenomena that make radar perception challenging in practice.

Bridging abstraction and physics

Radar simulation often forces a trade-off between abstraction and realism. Pure object-list models are easy to integrate but lack physics. Full 3D ray tracing models are realistic but complex to set up they ware very slow.

This approach bridges both worlds. By reconstructing the environment from ASAM OSI GroundTruth data, combining it with physical ray tracing, scattering center models, and Fourier-based signal processing, the model enables high-fidelity radar simulation without the burden of a predefined 3D environment.

👉 Learn more about the radar models here: Explore Persival radar models