Which coordinate interpolation method is better and why?

Question

satellite navigation

I decided to put it in a separate question. to consider possible solutions, it requires a separate consideration.
Question related to others

Dropping points is really quite primitive. The very collective farm that everyone uses is the Kalman filter to smooth out these saws. In a normal way, they use map matching, although you will have problems with pedestrians.

The problem is coordinate interpolation.
The goal is to reduce noise.
The result is the accuracy of the distance traveled and the user's positions.




Interested in the opinion of locals who did, examples and packages on JS. But they are suitable for other languages ​​too . It just takes more time to transfer the logic.

CHAIKIN'S ALGORITHMS FOR CURVES

Calman Filter and more description

Stack
Ramer–Douglas–Peucker algorithm

Stack answer Calman
and other packages that work with the same algorithm
Blog guide

This means we can reduce the noise by getting rid of all the locations with accuracy values ​​lower than a certain threshold. Also since we know the time elapsed between each reported location, we can use the Linear least squares method to get rid of anomalies in terms of calculated speed and acceleration.

There is supposedly an example here, but it is non-working, although it suggests different options and approaches to solving the problem. Who fumbles in React.js welcome.

Map matching
and package

1. Which approach is better and why?
   
2. What other ways are there?
   
3. What additions and combinations of counting algorithms are possible?
   
4. Or should you only use one?
   
5. If one, which one?