Bias-Correction In Localization Algorithms

Abstract

In this paper we introduce a new approach to determine the bias in localization algorithms by mixing Taylor series and Jacobian matrices, which results in an easily calculated analytical expression for the bias. To illustrate this approach, we analyze the proposed method in two situations using localization algorithms based on distance measurements. Monte Carlo simulations verify that the proposed method is consistent with the performance of localization algorithms, which means the bias-correction method can correct the bias in most situations except when there is a collinearity problem. Although the method is analyzed in distance-based localization algorithms, it can be extended to other kinds of localization algorithms.