Some type of Kalman filter is almost always the best solution to an estimation problem involving a dynamic system given your computer can handle the matrix inversion. Generally, Kalman filters optimally combine the previous estimate, the confidence of the previous estimate, sensor measurements, and sensor confidence together for the new state estimate.
The advantage of the complementary filter is its simplicity and ease of implementation. The complementary filter's disadvantage is its accuracy; it will never behave better than a well tuned Kalman filter.
I am unfamiliar with the other filters you've listed :/
For your system, I would recommend using an extended Kalman filter or an unscented Kalman filter, both are capable of handling the nonlinear equations that you'll need for dead reckoning.
Choosing filter parameters will vary depending on the filter you end up using. I would recommend looking at Optimal State Estimation by Dan Simon which goes over linear and nonlinear Kalman filters as well as choosing filter parameters.
side note: Dead reckoning is an unobservable system. Your state estimate will slowly drift from truth no matter how accurate your sensors are. This may not be a problem if you're just trying to track motion over a couple of seconds. However, if you're trying to navigate a map using only the two sensors you've listed, you're bound to run into trouble.