No clear winner. The survival gap is under 10 percentage points and the balanced-score gap is under 0.05, so neither SolvSRK nor the best baseline clears the win threshold. Either works — choose on cost, licensing, or integration effort. All verdicts →
Three-target beat-frequency superposition. States: [I1, Q1, I2, Q2, I3, Q3, phi_vco, agc]. Targets at R=50 m (f_b≈50 kHz), 200 m (≈200 kHz), 500 m (≈500 kHz). Bandwidth-driven stiffness from 10:1 frequency ratio.
Richards et al., 'Principles of Modern Radar' (2010) Ch. 7
Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.
def rhs(t, y):
d = np.empty(8)
total_power = 0.0
for k in range(n_tgt):
I_k = y[2 * k]
Q_k = y[2 * k + 1]
w = omega_total[k]
d[2 * k] = -w * Q_k
d[2 * k + 1] = w * I_k
total_power += I_k * I_k + Q_k * Q_k
d[6] = _TWO_PI * (f0 + chirp_rate * t)
agc = y[7]
d[7] = (n_tgt - agc * total_power) * inv_agc_tau
return dCanonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.
Fingerprint
Spread: low
Default noise: none
Recommendation snapshot
Clean best: SciPy Radau
Noisy best: SciPy BDF
Coverage
14 solver arms · clean + 5 noise levels
Ranked on survival, precision, and speed
20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: Three-Target FMCW Beat Superposition (three-target-fmcw-beat-superposition)
Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.
Self-reported by Resonix Labs · not independently verified
Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.
Objective
Best overall trade-off of survival, precision, and speed.
Noise level
| # | Solver | Survival | SCD | nfev | Wall | Score |
|---|---|---|---|---|---|---|
| 1 | SciPy RadauSciPy | 100% | 15.6 | 450,839 | 5.25 s | 0.990 |
| 2 | Vern7external | 100% | 15.3 | 107,092 | 4.01 s | 0.983 |
| 3 | Vern9external | 100% | 14.4 | 89,810 | 3.80 s | 0.961 |
| 4 | SciPy DOP853SciPy | 100% | 13.7 | 55,562 | 227 ms | 0.946 |
| 5 | Tsit5external | 100% | 13.7 | 130,146 | 6.80 s | 0.945 |
| 6 | SciPy RK45SciPy | 100% | 13.3 | 153,776 | 666 ms | 0.936 |
| 7 | SciPy LSODASciPy | 100% | 13.1 | 44,723 | 129 ms | 0.930 |
| 8 | SciPy RK23SciPy | 100% | 12.4 | 860,924 | 4.62 s | 0.915 |
| 9 | CVODE Adamsexternal | 100% | 12.1 | 40,346 | 204 ms | 0.907 |
| 10 | SciPy BDFSciPy | 100% | 11.9 | 96,043 | 1.94 s | 0.902 |
| 11 | CVODE BDFexternal | 100% | 11.8 | 53,531 | 275 ms | 0.899 |
| 12 | FBDFexternal | 100% | 9.9 | 136,891 | 5.17 s | 0.854 |
| 13 | SolvSRK | 100% | — | 145 | 123 ms | 0.809 |
| 14 | TRBDF2external | 100% | 7.7 | 5,945 | 3.48 s | 0.803 |
At Clean, best balanced arm is SciPy Radau · SolvSRK survival 100%.
Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →
SolvScout · free
This page shows one published benchmark cell. SolvScout fingerprints your ODE, compares it to the full corpus, and recommends a solver with the same survival / precision / speed ranking you see here — including when a SciPy arm wins.
SolvSRK · 30-day trial
SolvSRK is the stiffness-adaptive integrator behind the SolvSRK column in these tables. Create an account, activate a machine, and take a 30-day trial — same binary you'd ship after purchase.
Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is — a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.
@misc{resonix_evidence_three_target_fmcw_beat_superposition_2026,
title = {Resonix Evidence Portal: Three-Target FMCW Beat Superposition},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/three-target-fmcw-beat-superposition}},
note = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels
Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology
Self-reported by Resonix Labs · not independently verified · Verification status