Cooperative Interceptor 4v3 (straight-line)
ADVANTAGES2 · dim 21SolvSRK 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 →
4-interceptor vs 3-target cooperative PN guidance with deconfliction. Per-vehicle state [x,y,theta] (dim=21). Target maneuver: straight-line. d_miss=2m terminal cutoff. Inter-interceptor repulsive potential at d_safe=50m.
Problem definition
Zarchan, 'Tactical and Strategic Missile Guidance', Ch. 8; Shima & Rasmussen, 'Cooperative Interceptor Guidance'
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: float, y: np.ndarray) -> np.ndarray:
d = np.zeros(dim)
positions = np.empty((N_total, 2))
for k in range(N_total):
bk = k * _STATES_PER_VEHICLE
positions[k, 0] = y[bk + 0]
positions[k, 1] = y[bk + 1]
for i in range(N_interceptors):
bi = i * _STATES_PER_VEHICLE
x_m, y_m, th_m = y[bi + 0], y[bi + 1], y[bi + 2]
vx_m = V_m * np.cos(th_m)
vy_m = V_m * np.sin(th_m)
d[bi + 0] = vx_m
d[bi + 1] = vy_m
tgt_idx = N_interceptors + (i % N_targets)
bt = tgt_idx * _STATES_PER_VEHICLE
x_t, y_t, th_t = y[bt + 0], y[bt + 1], y[bt + 2]
dx = x_t - x_m
dy = y_t - y_m
r_sq = dx * dx + dy * dy
if r_sq > d_miss_sq:
r = np.sqrt(r_sq) + _EPS_RANGE
vx_t = V_t * np.cos(th_t)
vy_t = V_t * np.sin(th_t)
dlam_dt = (dx * (vy_t - vy_m) - dy * (vx_t - vx_m)) / (r * r)
V_c = -(dx * (vx_t - vx_m) + dy * (vy_t - vy_m)) / r
a_pn = N_pn * V_c * dlam_dt
else:
a_pn = 0.0
a_deconf_x = 0.0
a_deconf_y = 0.0
for j in range(N_interceptors):
if j == i:
continue
bj = j * _STATES_PER_VEHICLE
dxij = y[bi + 0] - y[bj + 0]
dyij = y[bi + 1] - y[bj + 1]
dist_sq = dxij * dxij + dyij * dyij
if dist_sq < d_gate_sq:
dist = np.sqrt(dist_sq) + _EPS_RANGE
separation = max(dist - d_safe, 0.1)
repulsion = 50.0 / (separation * separation)
repulsion = min(repulsion, 200.0)
a_deconf_x += repulsion * dxij / dist
a_deconf_y += repulsion * dyij / dist
a_total = a_pn + (a_deconf_x * np.sin(th_m) - a_deconf_y * np.cos(th_m))
d[bi + 2] = np.clip(a_total, -50.0, 50.0) / V_m
for j in range(N_targets):
bj = (N_interceptors + j) * _STATES_PER_VEHICLE
th_t_j = y[bj + 2]
d[bj + 0] = V_t * np.cos(th_t_j)
d[bj + 1] = V_t * np.sin(th_t_j)
d[bj + 2] = omega_t
return d- Parameters
- N_interceptors = 4
- N_pn = 3
- N_targets = 3
- N_total = 7
- V_m = 100
- V_t = 30
- _EPS_RANGE = 1e-06
- _STATES_PER_VEHICLE = 3
- d_gate_sq = 10000
- d_miss_sq = 4
- d_safe = 50
- dim = 21
- omega_t = 0
- Initial condition
- y(0) = [-300, 0, 1.57079632679, -100, 0, 1.57079632679, …] [shape=(21,), min=-300, max=3000]
- Horizon
- t ∈ [0, 60]
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: SolvSRK
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: Cooperative Interceptor 4v3 (straight-line) (cooperative-interceptor-4v3-straight-line)
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% | 8.9 | 11,321 | 274 ms | 0.832 |
| 2 | SciPy RadauSciPy | 100% | 6.5 | 13,128 | 720 ms | 0.775 |
| 3 | SciPy RK23SciPy | 100% | 6.5 | 5,906 | 243 ms | 0.774 |
| 4 | SciPy DOP853SciPy | 100% | 6.1 | 8,774 | 245 ms | 0.764 |
| 5 | SciPy BDFSciPy | 100% | 5.8 | 7,218 | 411 ms | 0.757 |
| 6 | SciPy LSODASciPy | 100% | 5.7 | 5,530 | 124 ms | 0.756 |
| 7 | SciPy RK45SciPy | 100% | 5.5 | 3,824 | 141 ms | 0.751 |
| 8 | CVODE BDFexternal | 100% | 5.5 | 4,484 | 188 ms | 0.749 |
| 9 | CVODE Adamsexternal | 100% | 5.2 | 3,228 | 135 ms | 0.743 |
| 10 | Tsit5external | 100% | 3.4 | 5,394 | 1.48 s | 0.699 |
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_cooperative_interceptor_4v3_straight_line_2026,
title = {Resonix Evidence Portal: Cooperative Interceptor 4v3 (straight-line)},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/cooperative-interceptor-4v3-straight-line}},
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