Strapdown INS 13-State (dim=13)

ADVANTAGES2 · dim 13

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 →

13-state strapdown inertial navigation mechanization. States: quaternion (4), NED velocity (3), NED position (3), gyro bias (3). Stiff from Earth-rate Coriolis coupling and fast/slow gyro dynamics. Maneuvering aircraft profile. Envelope ID 132.

Navigation & INS

Problem definition

Savage (2000) Strapdown Analytics; Titterton & Weston (2004) Strapdown Inertial Navigation

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_strapdown_ins(t, y):
    q = y[0:4]
    v = y[4:7]
    p = y[7:10]
    bg = y[10:13]

    qn = np.sqrt(q[0]**2 + q[1]**2 + q[2]**2 + q[3]**2)
    if qn > 0:
        q = q / qn

    q0, q1, q2, q3 = q

    omega_b = np.array([
        0.02 * np.sin(0.5 * t),
        0.01 * np.cos(0.3 * t),
        0.005 * np.sin(0.1 * t),
    ]) - bg

    omega_ie_n = np.array([
        _OMEGA_E * np.cos(_LAT0),
        0.0,
        -_OMEGA_E * np.sin(_LAT0),
    ])

    C_bn = np.array([
        [1 - 2*(q2**2 + q3**2), 2*(q1*q2 - q0*q3),     2*(q1*q3 + q0*q2)],
        [2*(q1*q2 + q0*q3),     1 - 2*(q1**2 + q3**2),  2*(q2*q3 - q0*q1)],
        [2*(q1*q3 - q0*q2),     2*(q2*q3 + q0*q1),      1 - 2*(q1**2 + q2**2)],
    ])

    omega_nb = omega_b - C_bn.T @ omega_ie_n

    dq = 0.5 * np.array([
        -q1*omega_nb[0] - q2*omega_nb[1] - q3*omega_nb[2],
         q0*omega_nb[0] + q2*omega_nb[2] - q3*omega_nb[1],
         q0*omega_nb[1] + q3*omega_nb[0] - q1*omega_nb[2],
         q0*omega_nb[2] + q1*omega_nb[1] - q2*omega_nb[0],
    ])

    f_b = np.array([
        2.0 * np.sin(0.2 * t),
        1.0 * np.cos(0.15 * t),
        -_G + 0.5 * np.sin(0.1 * t),
    ])

    f_n = C_bn @ f_b

    g_n = np.array([0.0, 0.0, _G])
    omega_en_n = np.array([v[1] / _R_EARTH, -v[0] / _R_EARTH, 0.0])

    dv = f_n + g_n - np.cross(2.0 * omega_ie_n + omega_en_n, v)

    dp = np.array([
        v[0],
        v[1],
        v[2],
    ])

    dbg = -bg / _GYRO_BIAS_TAU

    return np.concatenate([dq, dv, dp, dbg])
Parameters
  • _G = 9.80665
  • _GYRO_BIAS_TAU = 3600
  • _LAT0 = 0.785398163397
  • _OMEGA_E = 7.29212e-05
  • _R_EARTH = 6.371e+06
Initial condition
y(0) = [1, 0, 0, 0, 50, 0, …] [shape=(13,), min=-1000, max=50]
Horizon
t ∈ [0, 600]

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: high

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: Strapdown INS 13-State (dim=13) (strapdown-ins-13-state-dim-13)

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%
10.325,1881.62 s0.864
2SolvSRK
100%
8.74,411282 ms0.826
3SciPy RK45SciPy
100%
8.44,706223 ms0.820
4Tsit5external
100%
7.34,6981.40 s0.792
5SciPy DOP853SciPy
100%
7.23,818179 ms0.790
6CVODE Adamsexternal
100%
7.21,74084 ms0.789
7SciPy LSODASciPy
100%
6.63,013133 ms0.775
8CVODE BDFexternal
100%
6.53,504162 ms0.773
9SciPy RK23SciPy
100%
6.124,7731.25 s0.764
10SciPy BDFSciPy
100%
5.57,397611 ms0.750

At Clean, best balanced arm is SciPy Radau · SolvSRK survival 100%, SCD 8.7.

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_strapdown_ins_13_state_dim_13_2026,
  title        = {Resonix Evidence Portal: Strapdown INS 13-State (dim=13)},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/strapdown-ins-13-state-dim-13}},
  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