Discrete or continuous Kalman filter?
I don't have experience with continuous time kalman filters.  However from your description it sounds like you are making measurements of position and acceleration over time.  If these measurements are sampled, meaning you have individual measurements associated with time, you should be using the discrete time kalman filter.  I feel comfortable broadly stating if you are trying to implement the filter on a pc (matlab), microcontroller, fpga, or dsp based on a set of measurements you are implementing the discrete time filter.   

EDIT

Based on your edits, I more clearly understand your question.  I don't have experience with Matlab's built-in kalman filter functions but a quick read of the comments in kalmd seem to indicate to me you want to use kalman and not kalmd.  Below is a snippet pasted from kalmd that should make it clear.

%   The LTI system SYS specifies the plant data (A,[B G],C,[D 0]).
%   The continuous plant and covariance matrices (Q,R) are first 
%   discretized using the sample time Ts and zero-order hold 
%   approximation, and the discrete Kalman estimator for the 
%   resulting discrete plant is then calculated with KALMAN.


This text tells me the function is expecting a continuous function which it then evaluates/discretizes, and then calls the kalman function to create a discrete filter.  From the sounds of it you already have discrete data (measurements over time).  Hence kalman is what you want to use.