A semidefinite programming approach to hybrid localization using RSSI and TOA

Abstract

In this paper, we exploit the semidefinite programming and the Cramer Rao lower bound techniques to study the hybrid fusion of RSSI and TOA. A semidefinite program is developed to fuse these two location-dependent parameters. The Cramer Rao lower bound expressions are also developed in order to assess theoretical performances of the RSSI and TOA fusion. In order to evaluate this localization scheme, Monte Carlo simulations are carried out in a generic environment using realistic parameters extracted from an ultra wide band measurement campaign. The semidefinite approach is compared to the weighted least-squares, the maximum likelihood, and the Cramer Rao lower bound for the different schemes (i.e. sole RSSI, sole TOA, and hybrid RSSI+TOA). The importance of the fusion of RSSI and TOA is highlighted while assessing the different factors influencing the positioning accuracy.