Light Linear RSS-Based Sensor Localization with Unknown Transmit Power by Tikhonov-regularization

In this paper, we introduce a new linear received signal strength-based estimator for unknown node localization which its accuracy at low Signal-to-noise ratio (SNR) is better than many linear estimators and can compete with estimators based on the convex optimization, but it is much lighter than convex optimization-based estimators. The main ingredients in our proposed linear position estimator are to reformulate the localization problem in terms of Tikhonov-regularization and introduce a biased noise variable. The way that we apply for this reformulation avoids any possible linear approximation in which target position variables are involved, thus saving fair amount of information. The way we use to approximate the nonlinear terms leads us to a linear estimator to outperform most convex estimators in terms of both accuracy and speed in high power noise. The proposed algorithm is also indifferent to the transmit power and thus applicable to either known or unknown transmit power scenarios. In other words, another advantage of our scheme is its ability to compute a closed-form expression for estimate of as a by-product.Simulation results show the efficacy of the proposed algorithm in comparison to other methods for both typical RSS-based measurement data model and the modified model for indoor application.