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Processing two different sources with a single filter is not possible. As I understand you want to get single clean signal by using n noisy observations but Kalman filter it is not a dimension reduction method so you will get vector/scalar state from vector/scalar observations in same dimensions. After filtering noisy observations individually you should ...


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I am not use how you can success multiple sensors using batch filter, but, in sequential filtering you need to perform time update before each measurement. You can do it using the timetag information of each sensor measurement. You can modify the batch filter code however, I strongly recommend to write your own function depending on what you need.


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If you are new, you might use FilterPy and python. It is such a good start point since it has detailed explanation and manuel.


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I have found how to filter a signal using kalman filter in this repo : SimpleKalmanFilter!. That is the perfect library for 1D kalman filter that I was looking for. One can also get valuable info. in this article https://www.kalmanfilter.net/kalman1d.html


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If the acceleration readings are performed in an inertial (non rotating in particular) frame then yes it's sufficient to use the accelerations, but if the acceleration measurements are performed on the moving craft then no it's not possible alone by accelerometer readings. Eventhough it's sufficient to use those accelerations $a_x$, $a_y$, and $a_z$ on a ...


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but what are the steps when only one sensor is available and the physical model of the moving object is not available Then it's not a Kalman filter. A Kalman filter works because the system is observable. In hand-wavy terms, you need to have redundant information about your system states, either because you have actual redundant inputs, or because you ...


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