# Good Reference Problem to Test Filtering/Estimation Algorithms

I am looking to figure out if a current filter algorithm I have built could be useful for some problems I am looking into at work. It isn't a Kalman filter, but is instead making estimations using a Neural Network and the latest $N$ time series measurements for some state.

I have tested it on a 2D particle tracking problem I put together with what I believe to be fairly large Gaussian noise, and it has performed decent as far as I can see. An example figure is below:

However, I don't know if this sample problem is challenging enough to prove this algorithm works sufficiently well and I also don't have any reference algorithm results to compare against.

Is there any sample problems seen in the literature or elsewhere that I could run to test this filtering algorithm against so I have a reference of what's good or not?

• You could compare it to a moving average filter. – fibonatic May 12 '16 at 8:45
• I think the problem is okay. I'd say compare with Kalman filter and particles filter. I also recommend the you compare the performance in case of model mismatch (e.g. you assume linear model but true system is either not linear or not always linear). – ThP May 12 '16 at 9:01
• @ThP good thoughts! Particular with model mismatch. – spektr May 12 '16 at 14:05