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I've recently implemented a kalman filter to estimate altitude for a small robot with an IMU+Baro sensor mounted on it.

My objective is to get max precision I can have, using this two sensor, with small computing power that a MCU can provide me. I've tuned my filter and it seems to work pretty well.

Can I obtain a significant improvement using an Extended Kalman Filter instead of a normal Kalman Filter and if it worth time to implement it?

More in detail, since this request is too specific for each application, if a Model function that use Baro and Accel as states should be linearized and used in a EKF and if this can improve data reliability compared to a simply KF?

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  • $\begingroup$ You should check out UKFs in the next project that needs nonlinear estimation. They are way easier to implement, converge more reliably, and are only slightly more computationally expensive. $\endgroup$ – holmeski Jul 2 '16 at 18:47
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EKFs are appropriate when you have nonlinear equations describing the system, either in the system dynamics or the measurement dynamics. In this case, I think a plain KF should be sufficient assuming the accel measurements are just measuring gravity and veritcal acceleration.

If you expect your sensor to function well in a non level orientation where you are incorporating accel measurements from more than one axis on your IMU I would recommend using a nonlinear estimator.

If you are just estimating altitude with accel measurements of gravity plus motion and a pressure you should be more than okay with the linear KF.

I would also recommend estimating the bias in the IMU. These can, of course, be calibrated out at the start of a flight but estimating biases should be trivial.

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