- #1
Lindley
- 7
- 0
I want to use a Kalman filter to estimate the motion of an object. However, the catch is, the measurements I have only tell me that the object is somewhere along a particular line segment.
Typically Kalman filters require normally distributed measurements. I'm trying to work out how best to represent these line segments to the filter. Obviously, a 2D normal with extremely high covariance in the direction of the line would work to give relatively high likelihood to any point on the line segment; however, I also want likelihood to fall off quickly beyond the line, which won't occur in this case.
Any ideas?
Typically Kalman filters require normally distributed measurements. I'm trying to work out how best to represent these line segments to the filter. Obviously, a 2D normal with extremely high covariance in the direction of the line would work to give relatively high likelihood to any point on the line segment; however, I also want likelihood to fall off quickly beyond the line, which won't occur in this case.
Any ideas?