A point-target radar twin of two unresolved scatterers: it has the right median, so what does it get wrong, and does averaging looks converge where it says?

research/glint-twin

Point-target radar twin of two unresolved scatterers under ideal monopulse: real mean is exactly the strong scatterer and median exactly the power centroid, outside-span probability arccos(r)/π, a variance-matched Gaussian twin is 27× too optimistic at a 3σ gate (r=0.9), averaging looks floors at bias 2r²/(1+r²) where the twin predicts √(var/N), and the median beats the mean for r≥0.477. Stylised.

17 tests

What it found

Ideal monopulse on two unresolved scatterers (strong 1, weak r, random phase) gives Y=(1−r²)/(1+r²+2rcosφ): the mean is exactly the strong scatterer and the median exactly the power centroid, variance 2r²/(1−r²), and the estimate lies outside the target span with probability arccos(r)/π. A variance-matched Gaussian centroid twin gets the median right and the mean wrong by 2r²/(1+r²), is 27× too optimistic at a 3σ gate at r=0.9, and predicts √(var/N) for averaged looks where the real mean floors at that bias (0.90 vs 0.09 at N=1024); the median converges and beats the mean on variance for r≥0.477. Stylised: noise-free ideal monopulse, two scatterers, no radar data.

A figure from its own output

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

Paper, code and results

More in Digital twins & sim-to-real

A constant-variance Gaussian camera twin of Poisson photon counting: which dark/bright decision rule does it teach, and what does that cost on the real sensor?Constant-variance Gaussian camera twin of Poisson photon counting: the real Bayes threshold is the logarithmic mean, the twin's the arithmetic mean (7.28 vs 8.00 for 4 vs 12), a detector tuned in it has 1.25–1.83× the optimal error and 23× at λ0=200 (λ1=2λ0), and a square-root twin recovers the optimum in 8 of 9 pairs. Stylised.
A radar twin whose targets occupy a single range cell: how far around a strong target does the real matched filter ghost, and does tapering fix it?Single-cell radar twin vs a real matched filter: exact Rician ghost false alarms (0.020 at 20 dB, 1.0 at 30 dB for rect vs the twin's 1e-6), ghost zone radius growing √10 per 10 dB, taper gain exactly sidelobe difference + ENBW loss, and a 60 dB Hamming zone 6× Hann's despite the lower peak sidelobe. Stylised.
A link twin with independent packet loss says to send repeats back to back: what does burstiness do to repetition coding for shared maps, and what does spacing buy?Independent-loss link twin vs a bursty loss chain for repeated map updates: exact residual p·q_g^(r−1), the twin's 5 back-to-back copies leave 81–311× the claimed loss at λ=0.5–0.8, spacing needs 6–36 slots for two copies, and with ≤8 copies the best design is 12–154 slots of latency vs the twin's 4.
A lidar twin without atmospheric loss overstates range in fog: by how much, how sensitive is it to a mis-set extinction, and what does it do to a driving policy?Clear-air lidar twin vs fog: detection range W(αr0)/α overstated by exactly e^{αr}, range error is W/(1+W)<1 times the extinction error, a policy sized on median fog is violated 50% of the time (5%-quantile range: 5%, 43.5% slower), and one extinction calibrated on a bright target fails for dark ones in patchy fog. Stylised.
Is a clear-air or mean-rain-rate radar twin right when rain comes in cells?Clear-air or mean-rain-rate radar twin vs intermittent storm-cell attenuation: Jensen gap 1.52× in the rain-rate power law, Lambert-W mean-rate range reached by only 39% of real fields, mean-matching barely helps, a rain ensemble scores Brier 0.038 vs 0.101 clear air. Stylised.
How long must a real test run be when its length was sized in a twin with the wrong plant pole?Real test-run length sized in a wrong-pole twin: exact autocorrelated-cost variance, 3.8× too short (coverage 0.69 vs 0.95) or 2.1× too long depending on the real closed-loop pole, and a pilot-run repair that under-sizes half the time.

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

Other research topics

Proper scoring & elicitation28 Effort & peer prediction9 Verification games & slashing19 Detection & tolerances14 Markets as routers13 Prediction-market design9 Paying contributors8 Decentralised training systems23 Digital twins & sim-to-real164 LLM chip design loops40 Robot manufacturing loops53 Ambient AI assistant46 Self-replicating manufacturing34 Simulation-first design64 Engineered clothing45 Task-specific AI accelerators40 Algorithmic chip-design loops42 Open-source purpose-built robots44 The software-engineering loop43 Adversarial human imitation41 Open-source residential construction41 Autonomous multi-angle capture34 Sensing, imaging and 3D31 Energy supply and dense energy storage39 Robot societies among people31 Agentic co-design of low-cost robots27 Laundry-hanging robots25 Machine learning on qualitative data23 Robots on the building site27 Civilizations & group psychology1 History & 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.