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In trying to implement an Unscented Kalman Filter (UKF), I have come across the issue of what to do when my measurement signals come in at a different rate than my control inputs, which I use in the prediction step of the UKF. In my implementation, I've tried working around this issue by forming a prediction each time I receive a new measurement, but I've been told that it's also possible and perhaps favorable to only form a prediction when I get a new control input and form a new measurement update when I get a new measurement.

The "standard" Kalman filter algorithm in most textbooks/papers that I've seen do not delve deeply into this issue of different data rates, and they usually just show a simple predict-update loop that occurs at each time step.

I see how it is possible to form multiple consecutive predictions. However, I don't fully understand how one could do an update without a prediction right before it at the same time step, since the computation of the Kalman gain relies on the sigma points passed through the state transition function. Any help would be greatly appreciated!

For reference, I am trying to implement my UKF using the FilterPy Python library: https://filterpy.readthedocs.io/en/latest/_modules/filterpy/kalman/UKF.html#UnscentedKalmanFilter

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  • $\begingroup$ Your question isn't clear. Could you show your model equations and we'll solve it together? $\endgroup$ – Royi Jul 13 '18 at 22:16
  • $\begingroup$ @Royi, my model equation f(x, u, dt) for the state transition involves integrating velocities to yield change in position, and integrating acceleration control inputs u to yield change in velocity. Therefore, this equation has numerous dt terms. My measurement function h(x) does not have any dt terms. Does this help? $\endgroup$ – theo1010 Jul 16 '18 at 13:46
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The most typical linear KF operates on a continuous time constant matrix differential state variable equation that is put into a state transition form, i.e. the integration is solved between 2 points in time. If you use uniform equally spaced time intervals, the matrices in the state transition form are constant. The continuous time state variables aren’t constrained to be evaluated at regular times. If you do the integrations for the 2 time points the state transition equations are valid, you just need to do the integration for those 2 points. For time pairs that aren’t uniform, a new integration will typically be required at each time update. The equations can be deduced for the linear KF but a UKF is a way to do an EKF, so your state variable description is more approximate so smaller time steps are likely to have better fidelity.

This is a bit more involved than what a filter package can do.

If you can tolerate some delay and your controls are smooth, you can try interpolation to synchronize your inputs and just use a standard filter package. You might assume that your controls are zero order hold.

There are papers on “network KF” that deal with these synchronization problems including late and out of order inputs. those might be helpful.

If you need the best possible filter, you will most likely need to derive and implement a custom solution

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  • $\begingroup$ I understand how to handle the issue of irregular time steps for the integration in the state transition, and the filter package I'm using allows for such modification. However, filter package aside, I am curious about whether or not a KF can even work if you don't synchronize your prediction steps with your update steps. You suggest interpolating the control inputs so that the prediction steps are synchronized with the updates. Do you know of any other valid ways to do this? Thanks. $\endgroup$ – theo1010 Jul 13 '18 at 17:58
  • $\begingroup$ I suggested looking at the network kalman filter literature. as well. There is also the iterated EKF where you can use more than one iteration per update. $\endgroup$ – Stanley Pawlukiewicz Jul 13 '18 at 18:44

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