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

2026-07-30

AI Summary

This work addresses the challenge of irreproducibility in indoor millimeter-wave radar sensing, which arises from scene geometry, material properties, multipath effects, and hardware or signal processing discrepancies. To overcome this, we present the first signal-level, path-traceable digital twin platform for indoor millimeter-wave radar. By integrating Unreal Engine–based ray tracing with FMCW signal modeling, the platform generates complex-valued multi-channel received signal grids along with per-path contribution records. It shares common interfaces for receive channels and range-angle processing, enabling direct comparison between simulated and real-world measurements. The system supports path-level provenance across objects, materials, propagation events, and output intervals. Evaluated in an office environment across 22 locations and 154 poses, it recalls 70.8% of measured active geometric response regions within the central field of view, effectively revealing residual sources and establishing a diagnosable, reproducible workflow for practical deployment.

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.

Problem

• mmWave radar
• digital twin
• indoor sensing
• signal-level simulation
• reproducibility

Innovation

• signal-level digital twin
• mmWave radar
• path-attributed simulation
• FMCW measurement calibration
• indoor radar perception