Error-Reduced Elliptic Positioning via Joint Estimation of Location and a Balancing Parameter

Abstract

Elliptic positioning (EP) has been a topic of lively interest to localization practitioners owing to widespread adoption of the bistatic configuration in many location-enabling technologies nowadays. This letter addresses the problem of non-Gaussian error mitigation in EP, by formulating it as joint estimation of target position coordinates and a balancing parameter (BP) for the bias errors. An alternating minimization algorithm is put forward to break the original formulation down into a conventional weighted nonlinear least squares (WNLS) location estimator and a closed-form BP-update step. With its objective being properly decomposed, the WNLS subproblem is converted into the difference-of-convex programming framework, to which an efficient iterative solution based on the concave-convex procedure is applicable. Simulation results demonstrate that the proposed error-reduced EP approach can outperform a number of existing methods in terms of localization accuracy.