Radio Map Construction via Graph Signal Processing for Indoor Localization

Recently, fingerprint-based localization has become a promising solution for indoor positioning because of its great performance in complex multipath environments. However, the extensive time and labor effort of constructing the radio map has become the bottleneck that hinders the adaptation of fingerprint-based localization in practice. In this article, we propose a novel cost-efficient radio map construction scheme, which relies on the fingerprint measurements from only a small number of reference points (RPs) via graph signal sampling and recovery techniques. First, using the topological characteristics of RPs, we model the radio map as a graph and design the angle fingerprint for the band-limited graph signal. Subsequently, the radio map is built based on graph clustering, sampling set selection and signal recovery. Extensive simulations are performed in a geometry-based ray tracing signal propagation model, which demonstrates that the proposed method can recover the radio map with low-collection cost and outperform existing solutions in terms of fingerprint accuracy and localization performance.