The demand for accurate localization in complex
environments continues to increase despite the difficulty in extracting
positional information from measurements. Conventional
range-based localization approaches rely on distance estimates
obtained from measurements (e.g., delay or strength of received
waveforms). This paper goes one step further and develops
localization techniques that rely on all probable range values
rather than on a single estimate of each distance. In particular,
the concept of soft range information (SRI) is introduced,
showing its essential role for network localization. We then
establish a general framework for SRI-based localization and
develop algorithms for obtaining the SRI using machine learning
techniques. The performance of the proposed approach is quantified
via network experimentation in indoor environments. The
results show that SRI-based localization techniques can achieve
performance approaching the Cramer–Rao lower bound and
significantly outperform the conventional techniques especially
in harsh wireless environments.RYC-2016-1938
