witwin-radar 0.0.2

Witwin Radar - Radar signal simulation

WiTwin Radar - Differentiable Radar Simulator

A GPU-accelerated, differentiable FMCW radar simulator for generating synthetic radar data from 3D scenes. It combines Mitsuba ray tracing with custom CUDA kernels for scene simulation, signal generation, and downstream radar processing.

This module is derived from RF-Genesis.

Get Started

Python 3.10+ and an NVIDIA GPU are required. This package depends on the base witwin package.

pip install witwin[radar]

Quick Start

import numpy as np
import torch

from witwin.radar import Radar, RadarConfig
from witwin.radar.sigproc import process_pc, process_rd

# FMCW radar configuration.
config = {
    "num_tx": 3,
    "num_rx": 4,
    "fc": 77e9,
    "slope": 60.012,
    "adc_samples": 256,
    "adc_start_time": 6,
    "sample_rate": 4400,
    "idle_time": 7,
    "ramp_end_time": 65,
    "chirp_per_frame": 128,
    "frame_per_second": 10,
    "num_doppler_bins": 128,
    "num_range_bins": 256,
    "num_angle_bins": 64,
    "power": 15,
    "tx_loc": [[0, 0, 0], [4, 0, 0], [2, 1, 0]],
    "rx_loc": [[-6, 0, 0], [-5, 0, 0], [-4, 0, 0], [-3, 0, 0]],
}

# Use the recommended GPU backend.
radar = Radar(
    RadarConfig.from_dict(config),
    backend="dirichlet",
    device="cuda",
    position=(0.0, 0.0, 0.0),
    target=(0.0, 0.0, -5.0),
    fov=60.0,
)

point = np.array([[0.0, 0.0, -3.0]], dtype=np.float32)
velocity = np.array([[0.0, 0.0, 0.01]], dtype=np.float32)


def interp(t):
    # Return target intensity and position at time t.
    positions = torch.tensor(point + velocity * t, dtype=torch.float32, device=radar.device)
    intensities = torch.ones((positions.shape[0],), dtype=torch.float32, device=radar.device)
    return intensities, positions


# Simulate one frame, then extract point cloud and RD map.
frame = radar.mimo(interp, t0=0)
pc = process_pc(radar, frame)
rd, _, ranges, vels = process_rd(radar, frame)

Scene API

Use Radar(..., position=..., target=..., fov=...) to define the radar pose, and Scene.add_* methods for scene assembly.

from witwin.core import Material, Structure
from witwin.radar import Radar, RadarConfig, Scene

radar = Radar(
    RadarConfig.from_dict(config),
    backend="dirichlet",
    device="cuda",
    position=(0.0, 0.0, 0.0),
    target=(0.0, 0.0, -1.0),
    fov=60.0,
)
scene = Scene(device="cpu")

scene.add_structure(
    Structure(
        name="car_body",
        geometry=car_body_mesh,
        material=Material(eps_r=3.0),
    )
)
scene.add_mesh(name="wheel_fl", vertices=wheel_vertices, faces=wheel_faces, dynamic=True)
scene.add_structure_motion(
    "wheel_fl",
    rotation=RotationMotion(
        axis=(0.0, 1.0, 0.0),
        angular_velocity=32.0,
        origin=(0.0, 0.0, 0.0),
        space="local",
    ),
)

frame = radar.simulate(
    scene,
    sampling="triangle",
    motion_sampling="per_chirp",
)

Available mutating scene methods:

• Scene.add_structure(...)
• Scene.add_mesh(...)
• Scene.add_smpl(...)
• Scene.add_structure_motion(...)
• Scene.update_structure(...)
• Scene.set_structure_motion(...)
• Scene.clear_structure_motion(...)

Features

• Recommended backend: dirichlet
• Ray tracing through Mitsuba with differentiable scene support
• Shared-core geometry and structure primitives
• SMPL body support through Scene.add_smpl(...)
• Optional per-structure rigid motion with parent inheritance
• Multi-radar orchestration through Radar.simulate_group(...)
• Torch-native DSP pipeline for range/Doppler processing and point-cloud extraction
• Optional antenna pattern, polarization, noise-model, and receiver-chain configuration

Running Tests

cd radar
pytest tests/
pytest tests/ --gpu

Examples

Run the maintained Python examples from the radar/ root:

python -m examples.single_point
python -m examples.mesh_scene
python -m examples.humanbody
python -m examples.music_imaging
python -m examples.amass_pointcloud
python -m examples.gen_amass_video
python -m examples.rgbd_range_doppler --input path/to/depths.npy

amass_pointcloud and gen_amass_video additionally require AMASS BMLmovi data under data/BMLmovi_full/BMLmovi/. The rendering examples require mitsuba and CUDA; the SMPL examples also require models/smpl_models/. rgbd_range_doppler reads .npy/.npz depth or point-cloud sequences, and can read Azure Kinect .mkv files when pykinect_azure is installed. It assumes the depth camera view is the radar view by default.

Installation

Python 3.10+ and an NVIDIA GPU are required.

pip install witwin[radar]

Core dependencies include torch, numpy, slangtorch, tqdm, matplotlib, and scipy. Optional rendering dependencies are mitsuba and drjit.

Citation

If this module or its original RF-Genesis work is relevant to your research, please cite:

@inproceedings{chen2023rfgenesis,
  author = {Chen, Xingyu and Zhang, Xinyu},
  title = {RF Genesis: Zero-Shot Generalization of mmWave Sensing through Simulation-Based Data Synthesis and Generative Diffusion Models},
  booktitle = {ACM Conference on Embedded Networked Sensor Systems (SenSys '23)},
  year = {2023},
  pages = {1-14},
  address = {Istanbul, Turkiye},
  publisher = {ACM, New York, NY, USA},
  url = {https://doi.org/10.1145/3625687.3625798},
  doi = {10.1145/3625687.3625798}
}

License

MIT

Developer

Xingyu Chen