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?