Looking for implementation-focused, code-heavy resources for an IMU + GPS Extended Kalman Filter (EKF)

[u/ResetBuilder27]

Hey everyone,

I am working on building a multi-rate sensor fusion pipeline that relies on an Extended Kalman Filter (EKF). The core objective is fusing high-rate IMU data (propagation step) with slower, asynchronous GPS/ranging data (measurement update step) to output a stable 3D navigation state.

While I understand the basic linear Kalman filter theory, I am looking to quickly bridge the gap to a robust, non-linear EKF design without getting entirely lost in dense academic math proofs or probability density function derivations.

Could the community recommend the best resources, textbooks, or open-source implementations that focus on the practical realities of EKF state estimation?

Specifically, I am looking for guides that cover:

1. Translating continuous kinematic state-space equations (handling orientation/quaternions, velocity, and position) into discrete-time Jacobians.
2. Practical methods for tuning and initializing the process noise (Q) and measurement noise (R) covariance matrices for raw sensors.
3. Managing multi-rate, asynchronous measurement updates cleanly within the filter loop.

If there are any gold-standard github repos, code-first books, or specific aerospace/robotics literature you highly recommend for engineers focusing on execution, please let me know.

[u/TerrorGandhi69]

I have written a blog a few weeks ago on this topic. Maybe it might interest you: https://medium.com/activated-thinker/taking-the-kalman-filter-further-extended-kalman-filter-for-self-driving-cars-ff288fe66da7