Hello ladies and gentlemen
my name is Qun Niu. I'm a postdoctoral
researcher
from Sun Yat-sen University my presentation
is MAIL multi-scale attention-guided
indoor localization
using geomagnetic sequences
this is the outline of my presentation
indoor localization has a wide range of
compelling
applications to achieve accuracy
researchers have studied various
techniques for indoor localization
of all these techniques geomagnetism has
attracted
much attention lately mainly because it
is
pervasive relatively stable and
efficient to collect
many approaches leverage shape
comparison algorithms
such as dynamic time warping to compare
two geomagnetic sequences
despite the accuracy it has a few
limitations
first the dynamic time warping is
usually computational costly
with long sequences secondly
it pays more attention to global
geometrical features
and thus discarding local anomalies
leading to degraded accuracy
secondly with all various scales
anomalies
it is difficult to fuse them effectively
to address above problems we propose
MAIL
our key novelties are as follows we
propose an
efficient geomagnetic localization
network
we propose a scale based feature
extraction unit
to extract scale based features and
we also propose an attention-guided
fusion and a localization scheme
we briefly review our related work as
follows
recent arts are broadly divided into the
following two categories
discrete fingerprint based
geomagnetic localization
and the sequence based localization
while the second category enhances the
localization
accuracy using temporal correlations it
has a few limitations
first the computational cost is
relatively high with long sequences
secondly it tends to discard local
anomalies leading to compromised
accuracy
next we elaborate our multi-scale
localization network
our network consists of three major
components
a scale based feature extraction unit
adaptive attention generation unit and
the location estimation unit
we design a scale based feature
extraction unit
where we divide the geomagnetic
sequence
into a few subsequences using the gated
recurrent units
we extract a feature vector for each
subsequence
by feature concatenation we get a scale
based feature
\Phi_m for current scale m then we
normalize it and get a normalized scale
based
feature \Psi_m
due to the diversity of geomagnetic
anomalies
we proposed an adaptive attention
generation unit
where we regress an attention value for each
scale
using the gradient sequence \Tau and the
PReLu function
combined with multiscale feature vectors
f and their attention values w
we fuse them attentively and get a fused
feature vector F
then we use the linear regression to
estimate
the user location x^\hat in our training
stage
we use the Euclidean distance as our
loss function
in order to evaluate the performance of
the proposed approach
we have conducted extensive experiments
in three different
trial sites the cumulative density
functions of localization
error demonstrate that our approach
improves the
localization localization accuracy by a
large
margin compared with state-of-the-art
competing schemes
mainly because our multi-scale
fine-grained location
feature extraction and attention
guidance
we also evaluate several important
parameters on the mean localization
error
we repeat our experiments with and
without attention guidance
and compare the
localization error
the results show that our localization
error is lower with attention guidance
to summarize we propose an efficient
sequence based
geomagnetic localization network
we propose a scale based feature extraction
unit
to extract multi-scale features
thirdly we propose attention guidance
where we fuse the multi-scale features
attentively
and estimate our current location
in the future we plan to study
crowdsourcing
for efficient geomagnetic database
construction and update
we also plan to study efficient
deployment in mobile devices
this is the end of my presentation thank
you