turbo-ckf 0.1.1

Turbo-CKF: High-performance Cubature Kalman Filter

turbo-ckf

turbo-ckf is a Rust-backed Cubature Kalman Filter package for high-throughput prediction/update loops. Implemented here purely as an experiment after reading the paper.

What This Package Optimizes

• Fast prediction with built-in linear models:
  • predict_standard_model(...)
  • predict_standard_model_ckf(...)
  • predict_linear_model(F)
  • predict_linear_model_ckf(F)
• Fast AHRS update path:
  • update_paper_ahrs(...)

predict(...) and update(...) also run through Rust, but callback cost in Python can dominate if your models are heavy.

Callback Contract

Custom fx and hx must be vectorized:

• Input shape is (2 * dim_x, dim_x).
• fx output shape must be (2 * dim_x, dim_x).
• hx output shape must be (2 * dim_x, dim_z).

If you pass pointwise callbacks, TurboCKF raises immediately.

Install (Local Dev)

From turbo-ckf/:

bash turbo_ckf/setup_env.sh

Usage

from turbo_ckf import TurboCKF

kf = TurboCKF(dim_x=2, dim_z=1, dt=0.1, hx=hx_vectorized, fx=fx_vectorized)
kf.predict_standard_model("constant_velocity")
kf.update(z)

AHRS path:

kf.predict_linear_model(Fk)
kf.update_paper_ahrs(z6, sigma_acc2=1e-2, sigma_mag2=1e-2)

Research Basis

• This repo is an implementation of the KCKF AHRS equations described in:
  • Shing, Y. C., et al., "KCKF: A Fast and Stable Quaternion-Based Orientation Estimator", arXiv:2602.12283 (2026), https://arxiv.org/abs/2602.12283.
• Credit for the method belongs to the paper authors.

License

Dual-licensed under either:

• MIT (LICENSE-MIT)
• Apache-2.0 (LICENSE-APACHE)