  This paper proposes a combined network structure between convolutional neural
network (CNN) and long-short term memory (LSTM) quantifier for WiFi
fingerprinting indoor localization. In contrast to conventional methods that
utilize only spatial data with classification models, our CNN-LSTM network
extracts both space and time features of the received channel state information
(CSI) from a single router. Furthermore, the proposed network builds a
quantification model rather than a limited classification model as in most of
the literature work, which enables the estimation of testing points that are
not identical to the reference points. We analyze the instability of CSI and
demonstrate a mitigation solution using a comprehensive filter and
normalization scheme. The localization accuracy is investigated through
extensive on-site experiments with several mobile devices including mobile
phone (Nexus 5) and laptop (Intel 5300 NIC) on hundreds of testing locations.
Using only a single WiFi router, our structure achieves an average localization
error of 2.5~m with $\mathrm{80\%}$ of the errors under 4~m, which outperforms
the other reported algorithms by approximately $\mathrm{50\%}$ under the same
test environment.
