Iterative ToA-Based Localization of Wireless Transmitters Using Dirichlet-Kernel-Based Range Representation

Iterative localization is currently seen as an attractive solution to localize a transmitter in a cellular network. It has been shown that, by iterating between a range estimation step and a multi-lateration step, it is possible to refine the estimation in the first step, where only local information is used at iteration one. The iterative approach gets close to the performance of direct localization; nevertheless, it does not seem to converge to the direct localization performance for medium and low signal-to-noise-ratio values, due to the fact that it still suffers from loss of information due to projections and data representation. In this work, we propose to approximate the range log-likelihood at the base station with a Dirichlet kernel and to perform all the processing in a common xy-domain so that projections are no longer needed. We numerically show that our approach brings significant performance gains as compared to the time-of-arrival based iterative position estimation algorithm, getting really close to the performance of direct localization.