udacity

Converting acceleration from accelerometer... how?

I've been trying to consider converting acceleration measurements from accelerometer to velocity and distance.... But I've been trying has not reach any good success yet. hmm~

What I've found are some of these equations:




And here are also some constraints when calculating this... And they are more into suggesting on using GPS to get more accurate data.

Constant velocity:

Integration of an acceleration is not a velocity alone, but velocity due to acceleration over time, plus the constant initial velocity.

Therefore the rationale of integrating acceleration to determine velocity is invalid, unless the initial velocity is provable to be zero.

The answer: No, an accelerometer, or a gyroscope, can not be used to determine constant velocity, without some additional sensor, or baseline data i.e. initial velocity.

Calculating distances:

Getting a practical position estimate from this type of 9-axis sensor is not possible without the use of another sensor that uses an external reference such as GPS.

Theoretically, if you know the accelerations of an object in space and its initial position and velocity you will be able to calculate the objects new position by propagating the information about its acceleration and velocity back on to the initial position (i.e. integrating the acceleration twice). The reason that it is not possible in practice is that the accelerometer has noise. This noise will have a non-zero mean, so when integrating the acceleration signal the non-zero mean noise is continuously added and accumulates in the resulting velocity signal. This is seen as sensor drift. The velocity estimate starts out fairly correct but quickly drifts off due to this accumulated noise. Integrating a second time to get the position only worsens the situation by repeating the process.

By using an external reference such as a GPS, the Kalman filter can be used to combine the slow-updating GPS signal, and the fast-updating acceleration signal together to produce a reliable estimate of the position. The GPS has the effect of zeroing the drift that would be accumulated by performing the integration on the acceleration signal.

I would suggest taking a look at the Udacity Youtube videos that Khamey suggested. When learning the Kalman filter it helps to get a clear general overview of what the objective is and what the kalman filter is doing. Then the math and the actual steps of the algorithm will be much easier to understand. Another thing that is helpful when learning the Kalman filter is doing it for one state variable at a time instead of a whole state vector. This just helps focus your attention on what the Kalman filter is actually doing so that you don't get bogged down by the matrix algebra.

enabling a sensor in shimmer

Sometimes when you are trying to stream data from a 6 or 9-DOF shimmer, you might get only one data (i.e accelerometer data only). This might caused by the previous usage where you only enable a specific sensor instead of all available sensors in the Shimmer.

To enable it again, use Multi Shimmer Sync and enable all available sensors you want. Connect (and 'Start' to ensure it's really being enabled). After disconnect, try again on the 9DOF calibration software and check if all sensors are enabled.

Notes:
- Resetting the Shimmer doesn't seem to work... Not sure why.

:)