Location and tracking applications for high data rate UWB systems

This paper discusses a maximum likelihood (ML) estimator for the localization of mobile nodes in communication networks. The derived estimator is optimized for ranging measurements exploiting the received signal strength (RSS). For this purpose, the bias and uncertainties of the RSS based ranging procedure are analyzed, considering a path loss model of an indoor ultra-wideband (UWB) network under line of sight (LOS) conditions. The nonlinearity of the path loss model is first taken into account before the statistics of the observed RSS are approximated by a Taylor sequence of first order. The so found metrics describe a weighted least squares (WLS) method. The metrics of the estimator are analytically derived in closed-form. The performance of the derived estimator is investigated in Monte-Carlo simulations and compared with a simple least squares (LS) method and another method exploiting RSS fingerprints.