I am trying to implement Kalman filter for vehicle tracking in MATLAB. A Vehicle is moving in X direction with constant velocity. Initial state for vehicle =[x(t) v(t)].
I have to find position of vehicle after t=2 sec. Position of vehicle from GPS is corrupted by noise.
My question is: if I consider that there is no process noise, then will initial prediction matrix be equal to measurement noise matrix? I don't know how to initialise prediction matrix.
Any part of your state that is initialized from a measurement can have the corresponding variance initialized from the measurement variance. If your state has other variables (e.g. velocity) which aren't directly measured, then you'll have to put in educated guesses about how far wrong you could be. Variance has units of "state unit squared" (because variance is the square of standard deviation). So if your velocity estimate has a 68% chance (see: normal distribution) of being within +/-7mph, then the initial variance would be 49 miles^2/hour^2.
