Open AccessPLE‐based estimators for RSSD source localisation with unknown transmitted power in wireless sensor networks
Author(s) -
Heydari Ali,
Aghabozorgi Masoud Reza
Publication year - 2019
Publication title -
iet wireless sensor systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.433
H-Index - 27
eISSN - 2043-6394
pISSN - 2043-6386
DOI - 10.1049/iet-wss.2019.0006
Subject(s) - estimator , instrumental variable , weighting , mathematics , algorithm , cramér–rao bound , linear least squares , mean squared error , least squares function approximation , computer science , statistics , mathematical optimization , medicine , radiology
Source localisation based on the received signal strength difference (RSSD) for unknown transmitted power of the source is attractive due to the low cost and simple implementation. This study focuses on the pseudo‐linear estimator (PLE) with a low computational closed form solution. The authors present least squares calibration (LSC) estimator, linear least squares (LLS) estimator and their best linear unbiased estimator (BLUE) variants (namely, the BLUE‐LSC and BLUE‐LLS estimators), then they show all these estimators are biased estimators due to the correlation between system matrix and PLE noise vector. To overcome this problem, a bias elimination method is presented using closed instrumental variable (IV). To achieve the Cramer–Rao lower bound for sufficiently small noise scenario an improved weighting IV (IWIV) estimator is presented. At the end of this study, simulation results verify the theoretical development.