# Sensor fusion - Kalman for two identical position sensors

I require a little help in the implementation of my filter. I am currently working with two Leapmotion devices for the physiological study of hand vibrations. For this we call each of the devices "L leader" and "L support". I have problems with the C matrix, which is responsible for modeling the operation of the sensors. In my case, Leapmotion devices deliver data on position (x, y, z) and of course with this it is possible to detach the speed and use it in the filter, leaving L leader (Px, Py, Pz, Vx, Vy, Vz) and L support (Px, Py, Pz, Vx, Vy, Vz). This would then be as follows for the merger? What data should be put in Xk*?

How do I model matrix C?

Should the H matrix be a 6x6 identity matrix?

The matrix $$C$$ usually is denoted the output matrix of your system. So e.g. if you are interested in the 3 positions $$x,y,z$$ your matrix would look like $$C=\begin{bmatrix} 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 & 0 \end{bmatrix}$$
The matrix $$H$$ on the other hand, is the matrix describing uncertainties in your system (which could also be measurement noise) and has the dimension $$3\times r$$ for the case of a 3-dimensional output. $$r$$ is the dimension of your assumed uncertainty (could be 6 for the noise of your $$2\times3$$ dimensional sensors). In practice, it is feasible to assume the uncertainties are not affecting each other, in this case the matrix $$H$$ simplifies to a diagonal matrix.