Robust Elliptic Localization Using Worst-Case Formulation and Convex Approximation

Abstract

This article considers the problem of source localization (SL) using possibly unreliable timeof-arrival (TOA) based range measurements. Adopting the strategy of statistical robustification, we formulate the TOA SL as minimization of a versatile loss that possesses resistance against the occurrence of outliers. We then present an alternating direction method of multipliers (ADMM) to tackle the nonconvex optimization problem in a computationally attractive iterative manner. Moreover, we prove that the solution obtained by the proposed ADMM will correspond to a Karush-Kuhn-Tucker point of the formulation when the algorithm converges, and discuss reasonable assumptions about the robust loss function under which the approach can be theoretically guaranteed to be convergent. Numerical investigations demonstrate the superiority of our method over many existing TOA SL schemes in terms of positioning accuracy and computational simplicity. In comparison with its competitors, the proposed ADMM is in particular observed to produce location estimates with mean squared error performance closer to the Cramér-Rao lower bound in our simulations of impulsive noise environments.