I was recently introduced to the concept of Kalman filtering in the context of projectile tracking. A classmate recommended this to me, and what intrigued me most was its ability to fuse different types of information from sensors.
Are there types of measurements that are not compatible for sensor fusion? Can any measurement be fused to better inform the underlying model?
- Example: We take pictures at different time points of a rocket flying through the sky.
- We use some computer vision tool to automate the detection of the rocket's position from the photograph (with some degree of measurement error).
- At a series of frames, the rocket slowly disappears behind a cloud (undetectable) before re-emerging.
- We know the rocket exists and is positioned somewhere behind this cloud.
- Is it the (A) "boundary"/border of the cloud that can inform our state space of the rocket,
- ... or (B) the pixel intensity along the trajectory (suddenly shifting from blue to white) that can inform our state space of the rocket?
Intuitively, I feel both can better inform the trajectory, but they seem to me very different interpretations of the auxiliary information. I am not sure if there are limits of sensor fusion I cannot appreciate as an outsider to this field.