Kalman Filter Implementation with Constant Acceleration Model in Python from nbviewer python Watch Video

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⏲ Duration: 6 min 1 sec
✓ Published: 28-Feb-2014
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Here we have a GPS and a Acceleration Measurement, which have to be fused optimally via the Kalman FilternnSecond example is the same dynamic model but this time you measure the position as well as the acceleration. Both values have to be fused together with the Kalman Filter. Situation covered: You have an acceleration sensor (in 2D: x¨ and y¨) and a Position Sensor (e.g. GPS) and try to calculate velocity (x˙ and y˙) as well as position (x and y) of a person holding a smartphone in his/her

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Here we have a GPS and a Acceleration Measurement, which have to be fused optimally via the Kalman FilternnSecond example is the same dynamic model but this time you measure the position as well as the acceleration. Both values have to be fused together with the Kalman Filter. Situation covered: You have an acceleration sensor (in 2D: x¨ and y¨) and a Position Sensor (e.g. GPS) and try to calculate velocity (x˙ and y˙) as well as position (x and y) of a person holding a smartphone in his/her
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Short explanation of a Python implementation of a Kalman Filter for a constant acceleration model.nnSituation covered: You have an acceleration sensor (in 2D: x¨ and y¨) and try to calculate velocity (x˙ and y˙) as well as position (x and y) of a person holding a smartphone in his/her hand.nnIPython Notebook: http://nbviewer.ipython.org/github/balzer82/Kalman/blob/master/Kalman-Filter-CA.ipynb?create=1
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