Position estimation techniques for the local position measurement system LPM

The local position measurement system LPM is by now one of the most advanced systems in this field. In this contribution we discuss different techniques for estimating the object position for the LPM measurement principle. These techniques include the adaptation of nonlinear least squares techniques for position estimation, a closed analytical solution based on the Bancroft algorithm known from GPS signal processing, as well as a filter based approach based on a Kalman tracking filter without an additional position estimation algorithm. A focus was set on the geometrical interpretation of the basic circumstances and on the minimization criterions. The theoretical results are verified by an indoor test setup.