Geometric dilution of localization and bias-correction methods

Abstract

A particular geometric problem-the collinearity problem-which may prevent effective use of localization algorithms is described in detail in this paper. Further analysis illustrates the methods for improving the estimate for localization algorithms also can be affected by the collinearity problem. In this paper, we propose a novel approach to deal with the collinearity problem for a localization improvement method-the bias-correction method. Compare to earlier work such as, the main feature of the proposed approach is that it takes the level of the measurement noise into consideration as a variable. Monte Carlo simulation results demonstrate the performance of the proposed method. Further simulation illustrates the influence of two factors on the effect of the bias-correct method: the distance between sensors and the level of noise. Though it mainly aims to the bias-correction method, the proposed approach is also valid for localization algorithms because of the consistent performance of localization algorithms and the bias-correction method.