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 →
Wave 1 anti-UAV kill chain. Board approved 2026-05-07.
Skolnik (2008), Richards (2010)
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):
I_t, Q_t = y[0], y[1]
I_c, Q_c = y[2], y[3]
I_n, Q_n = y[4], y[5]
P_t, P_c = y[6], y[7]
xi_c_I = np.interp(t, _noise_ts, noise_table[0])
xi_c_Q = np.interp(t, _noise_ts, noise_table[1])
xi_n_I = np.interp(t, _noise_ts, noise_table[2])
xi_n_Q = np.interp(t, _noise_ts, noise_table[3])
d = np.empty(8)
# Target echo: damped sinusoid at Doppler frequency
d[0] = -TWO_PI * f_d * Q_t - (1.0 / tau_t) * I_t
d[1] = +TWO_PI * f_d * I_t - (1.0 / tau_t) * Q_t
# Clutter: near-zero Doppler with exponential decorrelation + noise
d[2] = -TWO_PI * f_c * Q_c - (1.0 / tau_c) * I_c + sigma_c * xi_c_I
d[3] = +TWO_PI * f_c * I_c - (1.0 / tau_c) * Q_c + sigma_c * xi_c_Q
# Thermal noise: independent I/Q (BA-RCT-2)
d[4] = -(1.0 / tau_n) * I_n + sigma_n * xi_n_I
d[5] = -(1.0 / tau_n) * Q_n + sigma_n * xi_n_Q
# Power estimates (smoothed envelope)
d[6] = (1.0 / tau_avg) * (I_t**2 + Q_t**2 - P_t)
d[7] = (1.0 / tau_avg) * (I_c**2 + Q_c**2 - P_c)
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: low
Recommendation snapshot
Clean best: SolvSRK
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: Pulsed Doppler I/Q — f_d=200Hz, clutter=30dB (pulsed-doppler-i-q-f-d-200hz-clutter-30db)
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 | SolvSRK | 100% | 8.1 | 111,648 | 676 ms | 0.811 |
| 2 | SciPy RadauSciPy | 100% | 6.2 | 96,362 | 2.71 s | 0.767 |
| 3 | FBDFexternal | 100% | 6.1 | 58,447 | 6.95 s | 0.764 |
| 4 | SciPy LSODASciPy | 100% | 5.6 | 44,089 | 433 ms | 0.753 |
| 5 | SciPy RK23SciPy | 100% | 5.4 | 44,099 | 667 ms | 0.747 |
| 6 | Vern7external | 100% | 5.3 | 126,932 | 7.34 s | 0.744 |
| 7 | Tsit5external | 100% | 5.1 | 68,304 | 7.59 s | 0.741 |
| 8 | Vern9external | 100% | 4.8 | 195,554 | 8.99 s | 0.733 |
| 9 | CVODE Adamsexternal | 100% | 4.8 | 24,487 | 330 ms | 0.732 |
| 10 | CVODE BDFexternal | 100% | 4.8 | 39,345 | 517 ms | 0.732 |
| 11 | SciPy RK45SciPy | 100% | 4.6 | 45,038 | 586 ms | 0.729 |
| 12 | SciPy DOP853SciPy | 100% | 4.6 | 126,326 | 1.55 s | 0.729 |
| 13 | SciPy BDFSciPy | 100% | 4.5 | 44,465 | 1.88 s | 0.726 |
| 14 | TRBDF2external | 100% | 1.4 | 8,606 | 5.63 s | 0.651 |
At Clean, best balanced arm is SolvSRK.
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_pulsed_doppler_i_q_f_d_200hz_clutter_30db_2026,
title = {Resonix Evidence Portal: Pulsed Doppler I/Q — f_d=200Hz, clutter=30dB},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/pulsed-doppler-i-q-f-d-200hz-clutter-30db}},
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