← All projectsGitHub
Digital twins & sim-to-real

A radar twin with no Doppler folding certifies an ego-velocity estimator at every speed: what does the folding sensor do to it, and when can it be unfolded?

research/alias-twin

A radar twin with no Doppler folding: the twin-tuned ego-speed estimator breaks at v_u−2σ with an exact closed-form bias (saturating at one fold off), and a wrapped-cost search unfolds it iff azimuths are diverse, with a confusion probability Φ(−√E₁/2σ) matching Monte Carlo.

10 tests

What it found

Real radar radial speeds wrap modulo 2vu (vu=10 m/s); the twin’s do not. The twin-tuned least-squares ego-speed estimator has RMSE 0.022 m/s in the twin and on the real sensor up to 9.5 m/s, then 0.18 (9.8), 1.9 (10) and 14.9 m/s (12). Its large-n bias is exact in closed form (−14.94 at 12 m/s, simulated −14.92) and saturates at −D, D=2vuΣc/Σc2, once every target is one fold off. A wrapped-cost grid search restores the twin’s accuracy to 60 m/s with no mode errors, provided the azimuths are diverse: the nearest wrong speed has residual energy 4vu2(n−(Σc)2/Σc2), independent of speed, and Φ(−√E/2σ) matches Monte Carlo (0.061 vs 0.054 at ±8°); at ±5° the search picks the wrong fold in 120 of 300 scenes. Stylised: stationary scene, known azimuths and vu, Gaussian noise, no multi-PRF.

A figure from its own output

Figure produced by research/alias-twin: rmse_vs_speed
alias-twin/experiments/rmse_vs_speed.svg, written by the project's own experiment code.

Paper, code and results

More in Digital twins & sim-to-real

All 164 Digital twins & sim-to-real projects →

Other research topics

Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.