How to get rid of noise and oscillations using Kalman filtering
First, I think your motion equations are wrong. Assuming a constant acceleration model, the transition matrix, $\mathbf{A}$, should look like that:
$$
\mathbf{A} = \left[\begin{array}&amp; 1 &amp; \Delta_t &amp; 0.5\Delta^2_t \\ 0 &amp; 1 &amp; \Delta_t\\ 0 &amp; 0 &amp; 1\end{array}\right]
$$

Next, for your question, if the measurement noise (defined by $\mathbf{R}$) and the model noise (defined by $\mathbf{Q}$) are Gaussian, then the Kalman filter is the linear MMSE. That means that it is the optimal linear estimator in the MMSE sense.
Playing directly with the filter's gain yields a simple exponential averaging of the data.  

If you have access, I would recommend Tracking and Data Association by Bar-Shalom and Fortmann. This is an excellent book that covers Kalman filtering.