Related papers: mmRadarTwin: A Measurement-Calibrated Signal-Level Digital Twin Platform for Indoor mmWave Radar

mmRadarTwin: A Measurement-Calibrated Signal-Level Digital Twin Platform for Indoor mmWave Radar

URL: http://arxiv.org/abs/2607.28108v1
Date: Thu, 30 Jul 2026 12:14:34 GMT
Title: mmRadarTwin: A Measurement-Calibrated Signal-Level Digital Twin Platform for Indoor mmWave Radar
Abstract summary: mmRadarTwin is a signal-level and path-attributed digital-twin platform for indoor mmWave radar.<n> mmRadarTwin links a real radar measurement branch with an Unreal Engine scene-simulation branch.<n>We evaluate mmRadarTwin in an office deployment using a commodity monostatic mmWave radar and mobile scene-capture hardware.
Score: 7.469753295999656
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Indoor mmWave radar perception is difficult to reproduce because measured range-angle responses depend on scene geometry, material response, multipath, hardware conventions, and signal processing. Existing ray-tracing and digital-twin tools often expose rendering, channel, or path-level quantities, while radar sensing requires complex signal products that can be processed and compared in the same domain as real FMCW measurements. We present mmRadarTwin, a signal-level and path-attributed digital-twin platform for indoor mmWave radar. mmRadarTwin links a real radar measurement branch with an Unreal Engine scene-simulation branch through a shared receive-channel and range-angle processing interface. The simulator writes complex multi-channel receive grids and exports per-path contribution records that identify the actor, material tag, propagation event, and output-bin support of each simulated return. We evaluate mmRadarTwin in an office deployment using a commodity monostatic mmWave radar and mobile scene-capture hardware. Across 154 measured poses spanning 22 radar locations, the current physics-only path-basis simulator recalls 70.8% of measurement-active geometry-supported response regions in the central usable field of view while exposing residuals caused by weak or missing path support, shifted responses, unsupported anchors, and missing physical mechanisms. Rather than claiming complete radar-map reconstruction or cross-room generalization, mmRadarTwin establishes a practical systems workflow for constructing, comparing, and diagnosing indoor radar digital twins.

Related papers

Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking [18.63898447592778]
We introduce RGBTR-Motion, a benchmark that pairs RGB, thermal, and radar streams with dense instance masks and temporally consistent identities.<n>We also develop SAM-Radar, an RGB, thermal, and radar-based segmentation and tracking framework built on SAM 3.
arXiv Detail & Related papers (2026-09-08T07:17:33Z)
RadarTwin: Scene-Specific mmWave Radar Simulation and Learning for Mobile Indoor Perception [2.0255467447384015]
RadarTwin is a framework for generating deployment-specific radar training data before real data collection.<n>We show that simulated and real radar share the same object-discriminative shape and material features.<n>A representation trained on simulation alone recognizes real objects at 2.5 times chance with no real radar labels, and a few labeled examples raise this to 95.3% on a 12-way recognition task.
arXiv Detail & Related papers (2026-06-24T05:50:26Z)
RadarSim: Simulating Single-Chip Radar via Multimodal Neural Fields [49.41438378775056]
Radars are an ideal complement to cameras, with cameras offering fine angular resolution and radars providing metric depth and robustness under adverse weather.<n>RadarSim produces sharper geometry and Doppler range frames than radar-only reconstructions.
arXiv Detail & Related papers (2026-05-25T21:03:40Z)
RLPR: Radar-to-LiDAR Place Recognition via Two-Stage Asymmetric Cross-Modal Alignment for Autonomous Driving [14.25906590501402]
Radar-to-LiDAR place recognition can localize radar scans within existing LiDAR maps.<n>We propose RLPR, a robust radar-to-LiDAR place recognition framework compatible with single-chip, scanning, and 4D radars.<n>Experiments on four datasets demonstrate that RLPR achieves recognition accuracy with strong zero-shot generalization capabilities.
arXiv Detail & Related papers (2026-03-09T03:28:44Z)
RadarGen: Automotive Radar Point Cloud Generation from Cameras [64.69976771710057]
We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery.<n>RadarGen adapts efficient image-latent diffusion to the radar domain by representing radar measurements in bird's-eye-view form.<n>We show that RadarGen captures characteristic radar measurement distributions and reduces the gap to perception models trained on real data.
arXiv Detail & Related papers (2025-12-19T18:57:33Z)
Simulate Any Radar: Attribute-Controllable Radar Simulation via Waveform Parameter Embedding [12.285004244174917]
SA-Radar is a radar simulation approach that enables controllable and efficient generation of radar cubes conditioned on customizable radar attributes.<n>We design ICFAR-Net, a 3D U-Net conditioned on radar attributes encoded via waveform parameters, which captures signal variations induced by different radar configurations.<n>Our framework also supports simulation in novel sensor viewpoints and edited scenes, showcasing its potential as a general-purpose radar data engine for autonomous driving applications.
arXiv Detail & Related papers (2025-06-03T17:58:28Z)
Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework [57.994965436344195]
Beamforming is a key technology in millimeter-wave (mmWave) communications that improves signal transmission by optimizing directionality and intensity.<n> multimodal sensing-aided beam prediction has gained significant attention, using various sensing data to predict user locations or network conditions.<n>Despite its promising potential, the adoption of multimodal sensing-aided beam prediction is hindered by high computational complexity, high costs, and limited datasets.
arXiv Detail & Related papers (2025-04-07T15:38:25Z)
Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar [62.51065633674272]
We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements. We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure.
arXiv Detail & Related papers (2024-05-07T20:44:48Z)
DART: Implicit Doppler Tomography for Radar Novel View Synthesis [9.26298115522881]
DART is a Neural Radiance Field-inspired method which uses radar-specific physics to create a reflectance and transmittance-based rendering pipeline for range-Doppler images. In comparison to state-of-the-art baselines, DART synthesizes superior radar range-Doppler images from novel views across all datasets.
arXiv Detail & Related papers (2024-03-06T17:54:50Z)
Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling [65.17711407805756]
Sionna is a GPU-accelerated open-source library for link-level simulations based on. Since release v0.14 it integrates a differentiable ray tracer (RT) for the simulation of radio wave propagation.
arXiv Detail & Related papers (2023-03-20T13:40:11Z)
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [73.80316195652493]
We tackle the problem of exploiting Radar for perception in the context of self-driving cars. We propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion.
arXiv Detail & Related papers (2020-07-28T17:15:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.