It sounds like you are building a Kalman Filter, yes? You may want to lower the number of states as they are interdependent. Really, the "state" of your object is just a position (x,y,z) and orientation (i.e., rotation about the axes xr,yr). The other states can be derived as derivatives.
As for determining the noise covariances, there are several options:
1) Make it up. This seems to be what most people do.
2) Come up with an analytical model based upon a deep understanding of your system and its sensors.
3) Measure it. This is probably the most practical.
Suppose you keep your object perfectly still. Measure the accelerometer outputs and compute the variance on each signal output due to noise. Repeat for other sensors. The process noise covariance is harder to get at.
Either way, this is pretty advanced work and unless you are in graduate school you are unlikely to "do this right" so you may just want to try approach #1 and make it up. If you set your variances too high then the sensor readings will tend to be over-ignored and most of the computation of state updates will be based upon your physical model of inertial updating. If you set your variances too low then the sensor readings will be trusted over the inertial model but if the sensors are noisy you will then get poor updating state estimates.
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