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We consider the problem of self-localization by a resource-constrained mobilenode given perturbed anchor position information and distance estimates fromthe anchor nodes. We consider normally-distributed noise in anchor positioninformation. The distance estimates are based on the log-normal shadowingpath-loss model for the RSSI measurements. The available solutions to thisproblem are based on complex and iterative optimization techniques such assemidefinite programming or second-order cone programming, which are notsuitable for resource-constrained environments. In this paper, we propose aclosed-form weighted least-squares solution. We calculate the weights by takinginto account the statistical properties of the perturbations in both RSSI andanchor position information. We also estimate the bias of the proposed solutionand subtract it from the proposed solution. We evaluate the performance of theproposed algorithm considering a set of arbitrary network topologies incomparison to an existing algorithm that is based on a similar approach butonly accounts for perturbations in the RSSI measurements. We also compare theresults with the corresponding Cramer-Rao lower bound. Our experimentalevaluation shows that the proposed algorithm can substantially improve thelocalization performance in terms of both root mean square error and bias.

RSSI-Based Self-Localization with Perturbed Anchor Positions