Received signal strength-based sensor localization in spatially correlated shadowing

Wireless sensor localization using received signal strength (RSS) measurements is investigated in this paper. Most studies for RSS localization assume that the shadowing components are uncorrelated. However in this paper, we assume that the shadowing is spatially correlated. Under this condition, it can be shown that the localization accuracy can be improved if the correlation among links is taken into consideration. Avoiding the maximum likelihood (ML) convergence problem, we derive a novel semidefinite programming (SDP) approach by converting the corresponding noncovex ML estimator into a convex one. The performance of the proposed SDP estimator is compared with the ML estimator and previously considered estimators. Computer simulations show that the proposed SDP estimator outperforms the previously considered estimators in both uncorrelated and correlated shadowing environments.