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:)
If you used a commercial GPS receiver to obtain your tracking data, the raw trilateration data from satellites are already processed &quot;using Kalman filtering (more common) or least-squares (less common) estimation algorithms&quot; (see https://insidegnss-com.exactdn.com/wp-content/uploads/2018/01/marapr13-Solutions.pdf). Pay attention also to the counterintuitive statement that the article proves in the section *No Silver Bullet&quot;: the differences between least-squares and Kalman are surprisingly minor for the generic application in GPS receivers.
With this in mind, additional post-processing is quite unlikely to improve your data set, but instead your data can deteriorate as a result of this post-processing. It seems you cannot denoise your data any better, but you can analyze your route (whether the highway route passes &quot;canyons&quot;, sharp corners or the like) and, knowing the features of your GPS receiver taken from the manufacturer's datasheet, estimate the precision of your tracking data.
The article referenced also gives a partial answer to your highlighted question about an &quot;alternative&quot;.