Filter measured position data with Kalman Filter

How can I implement a Kalman Filter on measured position data?

It is about the stationary case, that means over time I get multiple position data of an static object. The measuring is inaccurate so I want to use Kalman Filter to filter the measuring data so the accuracy increases.

Answer 1

[William Rose]

@Kevin Cheng,

If you do not have some model of how your position is changing over time, a model that you would update based on the latest observations, then a simple cumulative average IS a kind of Kalman filter. If you trust more rcent measurement more than less recent, then you'd want to weight recent measurements more, for example with exponentially delcining weights, which is easy to do with a first order IIR filter.

But it sounds like you do not want to weight recent measurements more, so just a cumulative average will be the way to go.