You have a state vector X made up of 6 components, the first two of which are the x and y position of an object; let's assume that the other 4 are their velocities and accelerations:
X = [x, y, v_x, v_y, a_x, a_y] t
In the Kalman filter, your next state, Xt+1, is equal to the previous state Xt multiplied by the transition matrix A, so with the transition matrix you posted, you would have:
x t+1 = x t + v_x t + 0.5 a_x t
y t+1 = y t + v_y t + 0.5 a_y t
v_x t+1 = v_x t + a_x t
v_y t+1 = v_t t + a_t t
a_x t+1 = a_x t
a_y t+1 = a_y t
Which are the discrete approximation of the equations of an object moving with constant acceleration if the time interval between the two states is equal to 1 (and that's why it makes sense to suppose that the other four variables are velocities and accelerations).
This is a Kalman filter that allows for faster variations in the velocity estimation, so it introduces a lower delay than a (4, 2, 0) filter, which would use a constant velocity model.