I'm totally fishing for help with my specific problem, but this should be useful for others too:
With reference to the concepts in this awesome explanation, it would be great if you could name the various components involved in your implementation of the Kalman filter. By connecting the function arguments to some specific entities you'll be helping people that never Kalman filtered before.
For instance, if I have an object that moves in 2D, its speed can change some, its direction can change some, and I know more or less how those two can change, how can I improve on a set of estimated locations using your package? Again, while this is obviously gonna serve me (see here for background), I'm certain it'll prove helpful for scores of other Kalman novices.
Thanks in advance!
I'm totally fishing for help with my specific problem, but this should be useful for others too:
With reference to the concepts in this awesome explanation, it would be great if you could name the various components involved in your implementation of the Kalman filter. By connecting the function arguments to some specific entities you'll be helping people that never Kalman filtered before.
For instance, if I have an object that moves in 2D, its speed can change some, its direction can change some, and I know more or less how those two can change, how can I improve on a set of estimated locations using your package? Again, while this is obviously gonna serve me (see here for background), I'm certain it'll prove helpful for scores of other Kalman novices.
Thanks in advance!