# Kalman Filter Implementation

• posted

Hi All,

I am looking to use a kalman filter to predict the motion of a throw ball. I was wondering if anybody knew of a java implementation of the broad outline of the equations involved in kalman filters, or any specific examples of a java app / set of classes that have a kalman filter.

I am new to this field so don't be too harsh if I say something stupid. I know that obviously the filter is specific to the system modelled, but I don't want to waste time and effort reinventing the wheel of the broader setup that kalman filter follow.

Secondly, does anybody want to suggest the probable matrices I require for the physics part of this task, with the ball moving under gravity and air resistance.

Adam

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• posted

To the best of my knowledge a Kalman filter relies on regular updates of various instrumentation readings during movement to do a best estimate. If you are merely trying to project the trajectory of a ball based on parameters at the time of the balls release, and no updates during the flight of the ball, then I don't think a Kalman filter is of any benefit.

TC

• posted

Hi There,

I have done some further reading, and now have some idea of what is required. And yes it is easy to implement it in a programming language, now that I know the controlling equations.

My question now is, that I have created a simple setup, with the ball just travelling at a constant velocity in the 3 directions. I am for the moment ignoring air resistance, gravity etc.

What I need now to do is calculate / state the initial values in the Q,P and R matrices. What do I do for this, pick some values that a reasonable? OR is there a better way? For Instance, I don't know the measured error for the initial point(s), only that say the error isn't going to metres wrong, more like a few cm.

• posted

You need to know some physics.

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Here you will find more than you ever wanted to know about trajectories.

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• posted

People often complain that Kalman filters have a large start up cost associated with them. What they are saying is that the it takes time for the Q, P and R matrices take a number of iterations to converge to somewhat constant values. The number of iterations is proportional to how poor your intitial estimates are.

You may also wish to look at a alpha-beta-gamma filter, which is like a Kalman filter but with constant gains. This works well if you have an accurate model of the dynamics.

-paul

Paul Oh Boondog Automation

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