RSS-Based Localization of Multiple Radio Transmitters via Blind Source Separation

Abstract

This letter proposes a methodology for counting and locating the nodes of an uncooperative wireless network using power measurements collected by sensors. The approach is blind, allowing the detection and localization of the nodes without knowing the network&#x2019;s specific features (i.e., the number of nodes, modulation type, and medium access control (MAC)). Because the signals captured by the radio-frequency (RF) sensors are additively mixed, blind source separation (BSS) is used to separate transmitted power profiles. Then, received signal strength (RSS) is extracted from the reconstructed signals and localization is performed through conventional least square (LS) and maximum likelihood (ML) techniques. Numerical results reveal that the BSS-ML approach reaches a rather low localization error in mild shadowing regimes, even when the ratio between the number of RF sensors and nodes, <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>, is close to 1. Finally, it is shown how the performance degradation introduced by the imperfect BSS is slight and that the root mean square error (RMSE) approaches the Cram&#x00E9;r-Rao lower bound (CRLB) when increasing <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>.