I'm reading up on Kalman filtering at the moment. In particular, I'm interested in using the "extended" and "unscented" variants for IMU sensor fusion and calibration.
In A comparison of unscented and extended Kalman filtering for estimating quaternion motion, quaternions are used to represent 3d rotation.
I understand unit quaternions can be used to represent a 3d rotation. They suit for representing absolute attitude (a rotation from a universal reference), relative rotation, or angular velocity (a rotation representing rate per second, or some other fixed time period).
However, this papers discuss using Runge-Kutta integration, specifically RK4. It uses RK4 with the quaternions but doesn't seem to furnish details of what this involves or why it is necessary. Here's the part of the paper that mentions it…
Given the state vector at step k − 1, we first perform the prediction step by finding the a priori state estimate xˆ−k by integrating equation 1 [f = dq/dt = qω/2] through time by ∆t (i.e., 1.0 divided by the current sampling rate) using a 4th Order Runge-Kutta scheme.
I've encountered Runge Kutta before for integrating positions in kinematics. I don't really understand how or why it would be needed here.
My naive approach would be to simply multiply the existing attitude q by the angular velocity ω to get the expected new q – I don't see why numerical integration is necessary here? Perhaps it's to "scale" the unit time ω to the change that occurs in in ∆t, but surely that can be done very simply by directly manipulating of ω (raising it to fractional power ∆t)?