A Deep Learning Approach for Generating Soft Range Information from RF Data
This work addresses indoor localization challenges for RF-based systems by providing a more accurate alternative to single-distance estimates.
The paper tackles the problem of generating soft range information from RF data for indoor localization, achieving high accuracy and significantly outperforming conventional techniques in NLOS detection and ranging error mitigation.
Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative for highly accurate localization that gives all probable range values rather than a single estimate of distance. We propose a deep learning approach to generate accurate SRI from RF measurements. In particular, the proposed approach is implemented by a network with two neural modules and conducts the generation directly from raw data. Extensive experiments on a case study with two public datasets are conducted to quantify the efficiency in different indoor localization tasks. The results show that the proposed approach can generate highly accurate SRI, and significantly outperforms conventional techniques in both non-line-of-sight (NLOS) detection and ranging error mitigation.