Alternatives to offline Kalman filtering

Recently I got into vehicle models and filtering in general and immediately faced with the following question.

I have the recorded GPS data from car driving on a highway. However, there is a substantial level of noise present, so I decided to filter this $$(x, y)$$ data. Currently I am using Kalman filtering (the extended one - bicycle model of the vehicle is assumed) for this purpose, but I somehow have a feeling that Kalman is more suited for online estimation/filtering. I have also tried various other approaches, including median and Savitzky–Golay filters, but they do not take dynamic model into the account which I think is the important information to use while filtering this data.

So, my question is: Is there an alternative to Kalman filter, where model dynamics can be specified, which is more suited for this purpose?

I just once again want to emphasize that I have solely measured time sequence (fixed interval) with $$x, y$$ coordinates and data needs to be filtered after drive is over (offline usage).

Thank you for your help and I apologize if my question is not stated well - this is my first post:)

• you can start with this refs: robotics.stackexchange.com/questions/11197/… and www-cdr.stanford.edu/dynamic/estimationGPS/avec2002ryu.pdf , if you feel that your question needs re-wording – V.V.T Jan 7 at 12:08
• All you have is the GPS lat/lon data? No IMU, odometer, steering, or other data? – TimWescott Jan 7 at 15:07
• Yes, that is right. All I have is GPS lat/long data – Mark Lumar Jan 7 at 15:47
• Is your data a timed sequence {$x_n, y_n, t_n$} / {$x(t_n), y(t_n)$} or just a set of scattered points {$x_n, y_n$}? If it is a timed sequence, is a time interval fixed or varying? – V.V.T Jan 8 at 3:19
• It is timed sequence, time interval is fixed – Mark Lumar Jan 8 at 5:37