  In the field of localization the linear least square solution is frequently
used. This solution is compared to nonlinear solvers more effected by noise,
but able to provide a position estimation without the knowledge of any starting
condition. The linear least square solution is able to minimize Gaussian noise
by solving an overdetermined equation with the MoorePenrose pseudoinverse.
Unfortunately this solution fails if it comes to non Gaussian noise. This
publication presents a direct solution which is able to use prefiltered data
for the LPM (RNL) equation. The used input for the linear position estimation
will not be the raw data but over the time filtered data, for this reason this
solution will be called direct solution. It will be shown that the presented
symmetrical direct solution is superior to non symmetrical direct solution and
especially to the not prefiltered linear least square solution.
