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Is there any method to for sensor fusion of accelerometer ,gyroscope and barrometer?


6DOF using accelerometer and gyroscopeHow to access accelerometer/gyroscope data from Javascript?Sensor Fusion on iOS DevicesIndoor Positioning System based on Gyroscope and AccelerometerUsing Android gyroscope instead of accelerometer. I find lots of bits and pieces, but no complete codeMadgwick sensor fusion on LSM9DS0Sensor Fusion of Accelerometer and Gyroscope in Unity 3DAccelerometer and Magnetometer sensor fusion to get Gyroscopic DataKalman filter sensor fusion for FALL detection: Accelerometer + Gyroscopegyroscope and accelerometer are inverted






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1















I want sensor fusion for accelerometer, gyroscope, and barometer.
so far I have estimate orientation using accelerometer and gyroscope.
now I want to combine the data from accelerometer and gyroscope with the barometer to find the vertical height.



so far I have estimate orientation using accelerometer and gyroscope.
now I want to combine the data from accelerometer and gyroscope with the barometer to find the vertical height.



 phi_hat_gyr(i) = phi_hat + dt * (p + sin(phi_hat) * tan(theta_hat) * q + cos(phi_hat) * tan(theta_hat) * r);
theta_hat_gyr(i) = theta_hat + dt * (cos(phi_hat) * q - sin(phi_hat) * r);


the result should in the form of vertical height.



thanks!










share|improve this question
























  • please any one can help me out in above mentioned problem ?

    – Asad Bashir
    Mar 28 at 5:13

















1















I want sensor fusion for accelerometer, gyroscope, and barometer.
so far I have estimate orientation using accelerometer and gyroscope.
now I want to combine the data from accelerometer and gyroscope with the barometer to find the vertical height.



so far I have estimate orientation using accelerometer and gyroscope.
now I want to combine the data from accelerometer and gyroscope with the barometer to find the vertical height.



 phi_hat_gyr(i) = phi_hat + dt * (p + sin(phi_hat) * tan(theta_hat) * q + cos(phi_hat) * tan(theta_hat) * r);
theta_hat_gyr(i) = theta_hat + dt * (cos(phi_hat) * q - sin(phi_hat) * r);


the result should in the form of vertical height.



thanks!










share|improve this question
























  • please any one can help me out in above mentioned problem ?

    – Asad Bashir
    Mar 28 at 5:13













1












1








1








I want sensor fusion for accelerometer, gyroscope, and barometer.
so far I have estimate orientation using accelerometer and gyroscope.
now I want to combine the data from accelerometer and gyroscope with the barometer to find the vertical height.



so far I have estimate orientation using accelerometer and gyroscope.
now I want to combine the data from accelerometer and gyroscope with the barometer to find the vertical height.



 phi_hat_gyr(i) = phi_hat + dt * (p + sin(phi_hat) * tan(theta_hat) * q + cos(phi_hat) * tan(theta_hat) * r);
theta_hat_gyr(i) = theta_hat + dt * (cos(phi_hat) * q - sin(phi_hat) * r);


the result should in the form of vertical height.



thanks!










share|improve this question
















I want sensor fusion for accelerometer, gyroscope, and barometer.
so far I have estimate orientation using accelerometer and gyroscope.
now I want to combine the data from accelerometer and gyroscope with the barometer to find the vertical height.



so far I have estimate orientation using accelerometer and gyroscope.
now I want to combine the data from accelerometer and gyroscope with the barometer to find the vertical height.



 phi_hat_gyr(i) = phi_hat + dt * (p + sin(phi_hat) * tan(theta_hat) * q + cos(phi_hat) * tan(theta_hat) * r);
theta_hat_gyr(i) = theta_hat + dt * (cos(phi_hat) * q - sin(phi_hat) * r);


the result should in the form of vertical height.



thanks!







height accelerometer gyroscope kalman-filter sensor-fusion






share|improve this question















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edited Mar 26 at 7:39









Ganesa Vijayakumar

7014 gold badges10 silver badges26 bronze badges




7014 gold badges10 silver badges26 bronze badges










asked Mar 26 at 6:37









Asad BashirAsad Bashir

62 bronze badges




62 bronze badges












  • please any one can help me out in above mentioned problem ?

    – Asad Bashir
    Mar 28 at 5:13

















  • please any one can help me out in above mentioned problem ?

    – Asad Bashir
    Mar 28 at 5:13
















please any one can help me out in above mentioned problem ?

– Asad Bashir
Mar 28 at 5:13





please any one can help me out in above mentioned problem ?

– Asad Bashir
Mar 28 at 5:13












1 Answer
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I think a Kalman Filter is quite suited to your task. The best simple explanation i know of can be found here. It will give you temporal filtering as well as sensor fusion.



The difficult part is setting up the transition/measurement matrices. Since your problem is non-linear you will have to use the extended Kalman Filter, which uses different transition/measurement matrices as the state changes.






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    1 Answer
    1






    active

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    active

    oldest

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    active

    oldest

    votes









    0














    I think a Kalman Filter is quite suited to your task. The best simple explanation i know of can be found here. It will give you temporal filtering as well as sensor fusion.



    The difficult part is setting up the transition/measurement matrices. Since your problem is non-linear you will have to use the extended Kalman Filter, which uses different transition/measurement matrices as the state changes.






    share|improve this answer



























      0














      I think a Kalman Filter is quite suited to your task. The best simple explanation i know of can be found here. It will give you temporal filtering as well as sensor fusion.



      The difficult part is setting up the transition/measurement matrices. Since your problem is non-linear you will have to use the extended Kalman Filter, which uses different transition/measurement matrices as the state changes.






      share|improve this answer

























        0












        0








        0







        I think a Kalman Filter is quite suited to your task. The best simple explanation i know of can be found here. It will give you temporal filtering as well as sensor fusion.



        The difficult part is setting up the transition/measurement matrices. Since your problem is non-linear you will have to use the extended Kalman Filter, which uses different transition/measurement matrices as the state changes.






        share|improve this answer













        I think a Kalman Filter is quite suited to your task. The best simple explanation i know of can be found here. It will give you temporal filtering as well as sensor fusion.



        The difficult part is setting up the transition/measurement matrices. Since your problem is non-linear you will have to use the extended Kalman Filter, which uses different transition/measurement matrices as the state changes.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Jun 4 at 12:39









        Richard K. WadeRichard K. Wade

        3045 bronze badges




        3045 bronze badges


















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