EKF filter normally has a predict + update step, I am curious - how do you evolve the covariance of the state without one of the steps?

In essence I want to evolve the state of an object using an IMU and I want to track the covariance at each update to get an estimate on what the error could be. I do not have a secondary sensor to give an update step if I use the IMU input as the control.

I believe this should be doable with EKF but I am unsure how to resolve the missing part.


1 Answer 1


Model your system as $\dot {\mathbf x} = f(\mathbf x, \mathbf u)$, where $\mathbf u$ is your IMU input. If it weren't for all the pesky rotations, you could model this as $\dot {\mathbf x} = \mathbf u$ (i.e., you're just integrating the IMU input).

In other words, model your system as something that gets rotation rate and acceleration "commands", and has a state vector (your starter state vector will have three each of velocity, position, and orientation, so it'll have 9 or -- if you use quaternions for rotation -- 10 states).

I'm pretty sure that a better way to represent this is by modeling the system as $\dot {\mathbf x} = g_{rot}(\mathbf x) \mathbf u$, where $g_{rot}(\mathbf x)$ just rotates the IMU command into the state vector's frame of reference.

In the case of never having some ground truth (such as a GPS position) to compare your filter to -- this will work, but you will find that your covariance will inexorably grow. The only way that such a system would be useful is if you could initialize it to some known state at start-up. Rather than having the covariance as a handy way of knowing how to set the gains when you do get a correction, it'll just be an indication to the user of how unreliable the data is.

Note that this is how "pure" inertial navigation works -- before the trip starts, the inertial nav system is started, and basically "told" "OK, you're sitting still at this point with respect to the Earth, and you're pointed this direction". Then everything is held steady for a while, then away you go.

Compared to any sort of reference signal fused with IMU data, the position estimate will degrade very quickly. "Navigation grade" IMUs are absurdly expensive (many thousands of $$) and the really good ones are typically export controlled, and all of this is for a reason. If you're using cell-phone quality IMUs, don't expect to hold an accurate position for very long at all.

I've done one GPS/IMU fusion effort; this was the approach I took and it worked well for me.

  • $\begingroup$ Thanks, but in this case I don't have a GPS signal so what would the measurement step consist of? $\endgroup$ Commented Oct 26, 2022 at 3:28
  • $\begingroup$ Do you have anything that can provide a reference measurement? If so, then you can use that instead of a GPS (early inertial navigation systems used star sightings, for instance). If you're running purely off of the IMU, then for all the folks who assume you're doing sensor fusion, please edit your question with this information. Basically, if you have no "side" information other than the IMU itself, then you have nothing to base a Kalman filter on. $\endgroup$
    – TimWescott
    Commented Oct 26, 2022 at 3:38
  • $\begingroup$ You should still be able to get an estimate on your error though shouldn't you? I'm trying to see how you can track error over time, it seems like EKF should be useable for this somehow? $\endgroup$ Commented Oct 26, 2022 at 3:41
  • $\begingroup$ Please edit your question with this query -- it's a substantive part of the question, and Stackexchange really wants such things to be in the question, not buried in comments. $\endgroup$
    – TimWescott
    Commented Oct 26, 2022 at 3:48
  • $\begingroup$ Ok I tried to modify if, but your answer is still useful as is. $\endgroup$ Commented Oct 26, 2022 at 4:09

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