On the Discretization Schemes in Map-Aided Indoor Localization

Abstract

Pedestrian dead reckoning (PDR) implemented on the discretized map is considered as an efficient solution for indoor localization. Finer map discretization can provide better localization accuracy at the cost of higher computation complexity. In this letter, we characterize the effect of map discretization in PDR-based indoor localization. In particular, the closed-form expressions for the localization errors are derived for two typical discretization schemes. We then optimize the quantization intervals by a minimax formulation, which can guarantee the localization accuracy for users with different step sizes. We show that the low-complexity scheme only allowing transitions between adjacent discretization points on the map can achieve a robust and accurate performance when the quantization interval is optimized. Numerical results validate the effectiveness of the proposed scheme.