3-Target Coordinated Turn Tracking

ADVANTAGES1 · dim 21

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 →

3 independent targets with coordinated-turn dynamics (7 states each: x, y, z, vx, vy, vz, a_mag). Turn rates 0.1, 0.3, 0.05 rad/s. Acceleration decays with tau_a=5s.

Target tracking

Problem definition

Bar-Shalom et al., 'Estimation with Applications to Tracking and Navigation' (2001), Ch. 11

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):
    d = np.empty(dim)
    for i in range(n_targets):
        base = 7 * i
        x   = y[base]
        yy  = y[base + 1]
        z   = y[base + 2]
        vx  = y[base + 3]
        vy  = y[base + 4]
        vz  = y[base + 5]
        a_m = y[base + 6]

        v_mag = np.sqrt(vx * vx + vy * vy + vz * vz)
        v_safe = max(v_mag, _VEL_FLOOR)
        omega = omegas_arr[i]

        d[base]     = vx
        d[base + 1] = vy
        d[base + 2] = vz
        d[base + 3] = -omega * vy + a_m * vx / v_safe
        d[base + 4] =  omega * vx + a_m * vy / v_safe
        d[base + 5] =               a_m * vz / v_safe
        d[base + 6] = -a_m * inv_tau_a

    return d
Parameters
  • _VEL_FLOOR = 1e-06
  • dim = 21
  • inv_tau_a = 0.2
  • n_targets = 3
  • omegas_arr = [0.1, 0.3, 0.05]
Initial condition
y(0) = [1000, 0, 500, 50, 100, 0, …] [shape=(21,), min=-500, max=2000]
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: extreme

Default noise: low

Recommendation snapshot

Clean best: SciPy Radau

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: 3-Target Coordinated Turn Tracking (3-target-coordinated-turn-tracking)

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

#SolverSurvivalSCDnfevWallScore
1SciPy RadauSciPy
100%
10.43,78095 ms0.868
2Vern9external
100%
10.46423.81 s0.866
3Vern7external
100%
10.27623.99 s0.863
4SolvSRK
100%
10.22,57460 ms0.862
5Tsit5external
100%
8.81,200862 ms0.828
6SciPy DOP853SciPy
100%
8.53744 ms0.821
7SciPy RK45SciPy
100%
8.31,28615 ms0.816
8CVODE Adamsexternal
100%
8.223610 ms0.815
9SciPy LSODASciPy
100%
8.25215 ms0.814
10FBDFexternal
100%
7.97614.77 s0.808
11SciPy RK23SciPy
100%
7.48,255111 ms0.795
12SciPy BDFSciPy
100%
6.889835 ms0.780
13CVODE BDFexternal
100%
6.656814 ms0.776
14TRBDF2external
100%
4.411,0245.65 s0.724

At Clean, best balanced arm is SciPy Radau · SolvSRK survival 100%, SCD 10.2.

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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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_3_target_coordinated_turn_tracking_2026,
  title        = {Resonix Evidence Portal: 3-Target Coordinated Turn Tracking},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/3-target-coordinated-turn-tracking}},
  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