Dear Community, I am wondering if there are articles/books/references proposing implementations of Kalman Filters for positioning based on a single accelerometer ? (no GPS, no camera, no gyro, no odometery...)
Dear Ahmed,
if you have only acceleromter measurement, then your system is not observable. For example assumming Constant Acceleration model (A = [1 T *T/2; 0 1 T; 0 0 1], C = [0 0 1]), then observability matrix is M = [0 0 1; 0 0 1; 0 0 1]. You can estimate position, but covariance will constantly growing.
Dear Ahmed,
In this case you can determine the position each time stamp (epoch) as a predicted value only and there is will not be any measurement model that you need for update stage. In this case, I think you can a condition like zero velocity update (ZUPT), but your positioning accuracy will still low.
Best wishes
Thank you for your answers. ZUPT could be a solution but cannot be generalized for all positioning applications.
Good question:
your positioning accuracy will still low ,so, you can only determine the position each time as a predicted value only and there is will not be any measurement model that you need for update stage
thank you and good luck
consult this work: Filtre de Kalman discret à la modélisation HydrologiqueFebruary 2019
- Publisher: Editions universitaires européennes
- ISBN: ISBN13: 978-613-8-46494-5 ISBN-10:613846494X
- Project:
- Multi-site modeling and prediction of annual and monthly precipitation
- 📷Samra Harkat
Hello,
For Kalman filter you need at least two sensors; the first in the prediction epoch and the second in the update step.
Good luck.
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