SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →
Dual-target proportional navigation engagement with time-sharing radar. States: [radar_state(6), ekf_target1(6), ekf_target2(6), pn_guidance(4), actuator(4), kinematics(2)]. Radar alternates target tracking at 10 Hz between targets. PN gain N=4, closing velocity model from CLRI-3D-S2. Actuator dynamics: 2nd-order with rate limits. Reduced from dim=40 CLRI-MULTI by using sequential time-sharing instead of simultaneous tracking.
Zarchan Ch. 4, 8; Bar-Shalom et al. (2001) Ch. 6, 11; Blackman & Popoli (1999) 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 _clri_multi_reduced_rhs(t: float, y: np.ndarray) -> np.ndarray:
dy = np.zeros(28)
radar = y[0:6]
ekf1 = y[6:12]
ekf2 = y[12:18]
pn = y[18:22]
act = y[22:26]
kin = y[26:28]
rng = max(radar[0], 1.0)
rng_rate = radar[1]
az_r = radar[2]
el_r = radar[3]
rcs = max(radar[4], 0.1)
snr = radar[5]
dy[0] = rng_rate
dy[1] = -_CLOSING_VEL * 0.01 * np.sin(0.1 * t)
dy[2] = (_TARGET_VEL * np.cos(0.3 * t)) / max(rng, 100.0)
dy[3] = (_TARGET_VEL * 0.5 * np.sin(0.2 * t)) / max(rng, 100.0)
dy[4] = -0.01 * (rcs - 1.0)
dy[5] = -0.5 * (snr - rcs / (rng * rng * 1e-6 + 1.0))
active_target = int(t * _RADAR_SWITCH_HZ) % 2
meas_phase = np.sin(2.0 * np.pi * _MEAS_UPDATE_HZ * t)
meas_gain = 0.5 * (1.0 + np.tanh(10.0 * meas_phase))
tgt1_true_x = 5000.0 - _CLOSING_VEL * t + 100.0 * np.sin(0.3 * t)
tgt1_true_y = 1000.0 + _TARGET_VEL * t * 0.1
tgt1_true_z = 500.0 + 50.0 * np.cos(0.2 * t)
tgt1_true_vx = -_CLOSING_VEL + 30.0 * np.cos(0.3 * t)
tgt1_true_vy = _TARGET_VEL * 0.1
tgt1_true_vz = -10.0 * np.sin(0.2 * t)
gain1 = meas_gain if active_target == 0 else 0.0
ekf_bw1 = 5.0 * gain1
dy[6] = ekf1[3] + ekf_bw1 * (tgt1_true_x - ekf1[0])
dy[7] = ekf1[4] + ekf_bw1 * (tgt1_true_y - ekf1[1])
dy[8] = ekf1[5] + ekf_bw1 * (tgt1_true_z - ekf1[2])
dy[9] = ekf_bw1 * (tgt1_true_vx - ekf1[3])
dy[10] = ekf_bw1 * (tgt1_true_vy - ekf1[4])
dy[11] = ekf_bw1 * (tgt1_true_vz - ekf1[5])
tgt2_true_x = 4000.0 - _CLOSING_VEL * 0.8 * t + 80.0 * np.cos(0.4 * t)
tgt2_true_y = -800.0 + _TARGET_VEL * t * 0.15
tgt2_true_z = 700.0 - 30.0 * np.sin(0.15 * t)
tgt2_true_vx = -_CLOSING_VEL * 0.8 - 32.0 * np.sin(0.4 * t)
tgt2_true_vy = _TARGET_VEL * 0.15
tgt2_true_vz = -4.5 * np.cos(0.15 * t)
gain2 = meas_gain if active_target == 1 else 0.0
ekf_bw2 = 5.0 * gain2
dy[12] = ekf2[3] + ekf_bw2 * (tgt2_true_x - ekf2[0])
dy[13] = ekf2[4] + ekf_bw2 * (tgt2_true_y - ekf2[1])
dy[14] = ekf2[5] + ekf_bw2 * (tgt2_true_z - ekf2[2])
dy[15] = ekf_bw2 * (tgt2_true_vx - ekf2[3])
dy[16] = ekf_bw2 * (tgt2_true_vy - ekf2[4])
dy[17] = ekf_bw2 * (tgt2_true_vz - ekf2[5])
int_x = 0.0
int_y = 0.0
int_z = 0.0
los1_x = ekf1[0] - int_x
los1_y = ekf1[1] - int_y
los1_z = ekf1[2] - int_z
r1 = np.sqrt(los1_x**2 + los1_y**2 + los1_z**2 + 1.0)
los1_rate_az = (ekf1[4] * los1_x - ekf1[3] * los1_y) / (r1 * r1)
los1_rate_el = ekf1[5] / r1
pn_cmd1_az = np.clip(_PN_GAIN * _CLOSING_VEL * los1_rate_az, -_ACCEL_LIMIT, _ACCEL_LIMIT)
pn_cmd1_el = np.clip(_PN_GAIN * _CLOSING_VEL * los1_rate_el, -_ACCEL_LIMIT, _ACCEL_LIMIT)
los2_x = ekf2[0] - int_x
los2_y = ekf2[1] - int_y
los2_z = ekf2[2] - int_z
r2 = np.sqrt(los2_x**2 + los2_y**2 + los2_z**2 + 1.0)
los2_rate_az = (ekf2[4] * los2_x - ekf2[3] * los2_y) / (r2 * r2)
los2_rate_el = ekf2[5] / r2
pn_cmd2_az = np.clip(_PN_GAIN * _CLOSING_VEL * los2_rate_az, -_ACCEL_LIMIT, _ACCEL_LIMIT)
pn_cmd2_el = np.clip(_PN_GAIN * _CLOSING_VEL * los2_rate_el, -_ACCEL_LIMIT, _ACCEL_LIMIT)
tau_pn = 0.05
dy[18] = (pn_cmd1_az - pn[0]) / tau_pn
dy[19] = (pn_cmd1_el - pn[1]) / tau_pn
dy[20] = (pn_cmd2_az - pn[2]) / tau_pn
dy[21] = (pn_cmd2_el - pn[3]) / tau_pn
w2 = _OMEGA_ACT * _OMEGA_ACT
two_zw = 2.0 * _ZETA_ACT * _OMEGA_ACT
blend = 0.5 + 0.5 * np.tanh(5.0 * (0.5 - (active_target % 2)))
cmd_az = blend * pn[0] + (1.0 - blend) * pn[2]
cmd_el = blend * pn[1] + (1.0 - blend) * pn[3]
dy[22] = act[1]
dy[23] = w2 * (cmd_az - act[0]) - two_zw * act[1]
dy[24] = act[3]
dy[25] = w2 * (cmd_el - act[2]) - two_zw * act[3]
heading_rate = 0.02
dy[26] = heading_rate * act[0]
dy[27] = heading_rate * act[2]
return dyCanonical 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: high
Default noise: low
Recommendation snapshot
Clean best: SolvSRK
Noisy best: SolvSRK
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: Reduced Dual-Target Interceptor (dim=28) (reduced-dual-target-interceptor-dim-28)
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% | — | 1,007,473 | 25.33 s | 0.809 |
| — | SciPy BDFSciPy | 0% | — | — | — | — |
| — | SciPy RadauSciPy | 0% | — | — | — | — |
| — | SciPy RK45SciPy | 0% | — | — | — | — |
| — | SciPy LSODASciPy | 0% | — | — | — | — |
| — | SciPy DOP853SciPy | 0% | — | — | — | — |
| — | SciPy RK23SciPy | 0% | — | — | — | — |
| — | CVODE BDFexternal | 0% | — | — | — | — |
| — | CVODE Adamsexternal | 0% | — | — | — | — |
| — | Tsit5external | 0% | — | — | — | — |
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 →
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SolvSRK · 30-day trial
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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_reduced_dual_target_interceptor_dim_28_2026,
title = {Resonix Evidence Portal: Reduced Dual-Target Interceptor (dim=28)},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/reduced-dual-target-interceptor-dim-28}},
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