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I have the model for a gyroscope from an IMU given by

$$ \omega_{meas} = (I-S)\omega_{true} + \beta + v $$

$S$ is the scaling and misalignment of the sensor, which I understand is a parameter I get from datasheet. $\omega_{true}$ is the true value of the gyro rates, $\beta$ is the bias defined by the bias instability, and $v$ is the noise defined by the ARW parameter and bias instability.

After installation of the IMU, there will be an error due to structural misalignments between the IMU and whatever my structure will be. So, this structural error, $R_{struct}$, will generate a frame rotation, so I am a bit confused on how this error has to be modelled. I modelled it as affecting the whole measurement:

$$ \omega_{meas} = R_{struct}\left((I-S)\omega_{true} + \beta + v\right) $$

But someone told me it has to be applied only to the true measurement:

$$ \omega_{meas} = (I-S)R_{struct}\omega_{true} + \beta + v $$

I think is has to be applied on the whole system as otherwise, even do small, I will be applying the bias and noise in another frame.

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It's a little difficult to visualize rotations of a rotation sensor, so let's consider an accelerometer instead. You've got the same problem with an accelerometer: if the sensor isn't aligned with the chassis frame, you introduce a relative rotation $R_{struct}$.

Now, let's suppose some properties to go along with the accelerometer. We'll say it measures x- and y-axis accelerations, where +x is right/starboard and +y is forward. +z is up.

Let's also say that accelerometer has a y-axis bias of $1$ and an x-axis bias of 0. The chassis frame is currently accelerating at a rate of 1 $g$ forward.

If the accelerometer is well aligned to the chassis such that $R_{struct} = I$, then you get:

$$ a_{meas} = \left[\begin{array}{ccc} 0 \\ g \\ \end{array}\right] + \left[\begin{array}{ccc} 0 \\ 1 \\ \end{array}\right] $$

That is, for a well-aligned accelerometer, the x-axis output is $0$, from the fact that the chassis isn't accelerating along the x-axis, plus $0$, from the fact that the bias on the x-axis of that sensor is also zero.

The y-axis output is $g + 1$, from the chassis motion and the bias along the y-axis.

NOW, consider what happens if you rotate the sensor by +90 degrees about the z-axis. The sensor (+y) is now oriented along the chassis (-x) and the sensor (+x) is now oriented along the chassis (+y). Repeat the same measurement and now you get a reading of:

$$ a_{meas} = \left[\begin{array}{ccc} g \\ 0 \\ \end{array}\right] + \left[\begin{array}{ccc} 0 \\ 1 \\ \end{array}\right] $$

The chassis is accelerating at a rate of $g$ along its +y-axis, which the sensor is registering as a +x motion. However, the sensor is going to output a y-axis bias of $+1$ regardless of the orientation of the sensor.

That is, if there were no bias, your reading would be:

$$ a_{unbiased} = R_{struct}a_{chassis} \\ $$

The sensor bias is independent of the sensor orientation, so you add that to the unbiased reading:

$$ a_{biased} = a_{meas} = a_{unbiased} + bias \\ \boxed{a_{meas} = R_{struct}a_{chassis} + bias} \\ $$

Lastly, I believe if the bias were applied to all parameters, as you wrote:

$$ \omega_{meas} = R_{struct}\left(\left(I-S\right)\omega_{true} + \beta + \nu\right) \\ $$

That you could distribute the $R_{struct}$:

$$ \omega_{meas} = R_{struct}\left(I-S\right)\omega_{true} + R_{struct}\beta + R_{struct}\nu \\ $$

And then you could cleverly rotate your sensor to an angle of $\theta_{clever} = \mbox{atan2}\left(\beta_y,\beta_x\right)$ and have the entirety of the bias occur on one axis only and have the other axis be bias-free. That is, if the bias is based on sensor orientation, it would then become possible to affect bias or noise by altering the orientation of the sensor.

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