Robust Coarse Position Estimation for TDOA Localization

The non-linear least squares (NLLS) algorithm is widely used in localization systems. Its performance approaches the Cramer-Rao lower bound under i.i.d. additive white Gaussian noise. However, when the initial position chosen is not close enough to the actual target position, the NLLS algorithm will very likely diverge. The non-iterative method of moments estimator does not have this divergence problem, but it performs worse than NLLS and requires at least one more anchor to linearize the range measurement equations. In this paper, we develop a coarse position estimation algorithm based on scaling by majorizing a complicated function for time-difference-of-arrival localization, which is robust with regard to the initial position and does not require redundant receivers.