  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.
