Sources localization via multi-dimesional scaling based on subspace method

There is no doubt that the issue of estimate locations for unknown sources has been mattering a lot of debate in recent years, in the field of communication, Radar, Navigation, wireless sensor networks, …etc. In this paper, the multidimensional scaling (MDS) based on subspace method was applied to solve the addressed issue in the presence of distance information that acquired from TOA measurements. The Simulation results show the significant consequences compared with Cramer-Rao lower bound (CRLB) as well as the modified version of classical Multi-dimensional scaling, it became close to the lower bound of variance when the signal to noise ratio increased.