  Ultra-wideband (UWB) devices are widely used in indoor localization
scenarios. Single-anchor UWB localization shows advantages because of its
simple system setup compared to conventional two-way ranging (TWR) and
trilateration localization methods. In this work, we focus on single-anchor UWB
localization methods that learn statistical features of the channel impulse
response (CIR) in different location areas using a Gaussian mixture model
(GMM). We show that by learning the joint distributions of the amplitudes of
different delay components, we achieve a more accurate location estimate
compared to considering each delay bin independently. Moreover, we develop a
similarity metric between sets of CIRs. With this set-based similarity metric,
we can further improve the estimation performance, compared to treating each
snapshot separately. We showcase the advantages of the proposed methods in
multiple application scenarios.
