Information Modeling

Question

• The sensor module in my project consists of a rotating camera, that collects noisy information about moving objects in the surrounding environment.
• The information consists of distance, angle and relative change of the moving objects..
• The limiting view range of the camera makes it essential to rotate the camera periodically to update environment information...

I was looking for algorithms / ways to model these information, in order to be able to guess / predict / learn motion properties of these object..

My current proposed idea is to store last n snapshots of each object in a queue. I take weighted average of positions and velocities of moving object, but I think it is a poor method...

Can you state some titles that suit this case?

Thanks

Answer 1

Kalman {Extended, unscented, ... } filters and particle filters only after reading about Kalman filters.

Kalman filters learn and predict the correct data from noisy data with a Gaussian assumption, so it may be of use to you. If you need non-Gaussian methods, look at the particle filter.