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Asked: 2026-05-15T07:22:10+00:00

The sensor module in my project consists of a rotating camera , that collects

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  • 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

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1 Answer

  1. Editorial Team

    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.

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