Pulsed Doppler I/Q — f_d=500Hz, clutter=50dB
DISADVANTAGES1 · dim 8A baseline wins. At the comparison noise level, the best baseline beats SolvSRK by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use the winning baseline named on the problem page - not SolvSRK. All verdicts →
Wave 1 anti-UAV kill chain. Board approved 2026-05-07.
Problem definition
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.
Show canonical RHS excerpt
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 d- Parameters
- TWO_PI = 6.28318530718
- _noise_ts = [0, 0.0001, 0.0002, 0.0003, 0.0004, 0.0005, …] [shape=(1001,), min=0, max=0.1]
- f_c = 5
- f_d = 500
- noise_table = [0.125730221093, -0.132104863291, 0.640422650443, 0.104900117153, -0.535669373161, 0.361595054909, …] [shape=(4, 1001), min=-3.89942173005, max=3.25719907472]
- sigma_c = 316.227766017
- sigma_n = 1
- tau_avg = 0.01
- tau_c = 0.02
- tau_n = 0.001
- tau_t = 0.05
- Initial condition
- y(0) = [1, 0, 316.227766017, 0, 0, 0, 1, 100000]
- Horizon
- t ∈ [0, 0.1]
Canonical 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: medium
Recommendation snapshot
Clean best: SolvSRK
Noisy best: SciPy Radau
Coverage
14 solver arms · clean + 5 noise levels
Ranked on survival, precision, and speed
Versions & freeze
Methodology →- Freeze
- 2026-08-13
- libsolvsrk
- 2.3.0
- SciPy
- 1.14
- SUNDIALS
- CVODE (bundled backend)
20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: Pulsed Doppler I/Q — f_d=500Hz, clutter=50dB (pulsed-doppler-i-q-f-d-500hz-clutter-50db)
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
Results matrix
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% | 9.1 | 124,829 | 760 ms | 0.835 |
| 2 | FBDFexternal | 100% | 7.2 | 83,598 | 7.06 s | 0.789 |
| 3 | SciPy LSODASciPy | 100% | 7.0 | 49,159 | 500 ms | 0.787 |
| 4 | SciPy RK23SciPy | 100% | 6.9 | 78,242 | 878 ms | 0.784 |
| 5 | SciPy BDFSciPy | 100% | 6.4 | 57,349 | 2.51 s | 0.771 |
| 6 | CVODE Adamsexternal | 100% | 6.1 | 29,166 | 386 ms | 0.765 |
| 7 | Vern9external | 100% | 5.9 | 220,370 | 9.19 s | 0.759 |
| 8 | SciPy RadauSciPy | 100% | 5.8 | 105,341 | 3.02 s | 0.757 |
| 9 | CVODE BDFexternal | 100% | 5.7 | 47,596 | 628 ms | 0.756 |
| 10 | Vern7external | 100% | 5.5 | 123,902 | 7.36 s | 0.751 |
| 11 | Tsit5external | 100% | 5.2 | 69,048 | 7.68 s | 0.742 |
| 12 | SciPy RK45SciPy | 100% | 4.7 | 46,160 | 608 ms | 0.730 |
| 13 | SciPy DOP853SciPy | 100% | 4.5 | 136,634 | 1.50 s | 0.727 |
| 14 | TRBDF2external | 100% | 2.6 | 12,953 | 5.85 s | 0.680 |
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 →
Cite this page
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_500hz_clutter_50db_2026,
title = {Resonix Evidence Portal: Pulsed Doppler I/Q — f_d=500Hz, clutter=50dB},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/pulsed-doppler-i-q-f-d-500hz-clutter-50db}},
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.}
}Related
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