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Cooperative geolocation has attracted significant research interests inrecent years. A large number of localization algorithms rely on theavailability of statistical knowledge of measurement errors, which is oftendifficult to obtain in practice. Compared with the statistical knowledge ofmeasurement errors, it can often be easier to obtain the measurement errorbound. This work investigates a localization problem assuming unknownmeasurement error distribution except for a bound on the error. We firstformulate this localization problem as an optimization problem to minimize theworst-case estimation error, which is shown to be a non-convex optimizationproblem. Then, relaxation is applied to transform it into a convex one.Furthermore, we propose a distributed algorithm to solve the problem, whichwill converge in a few iterations. Simulation results show that the proposedalgorithms are more robust to large measurement errors than existing algorithmsin the literature. Geometrical analysis providing additional insights is alsoprovided.

Robust Localization Using Range Measurements with Unknown and Bounded Errors