hello everyone i'm Wei Sun. Today I will
present our work DeepRange acoustic
ranging by deeper learning this is a
joint work with Wenguang Mao, Mei Wang and Lili
Qiu from UT Austin Ranging performs a
critical role in human computer
interaction
many applications are based on the
capability of accurate ranging for
example vr and ar devices use ranging to
detect hand movement
smart speaker and car can detect user's
hand gesture with ranging
we focus on acoustic ranging on mobile
devices it is increasingly popular in
recent years because it has unique
advantages compared to other methods
a pair of speaker and microphone are
equipped on most devices the speaker can
send out the signal and the microphone
can detect a reflection to measure the
time of fly
besides it consumes less power
considering the low sampling rate
it is critical to mobile device
Acoustic has a centimeter level
wavelength so that it can achieve better
resolution than bluetooth and wi-fi
finally acoustic has a broad effective
range it is a good fit for indoor
applications
there have been many signal processing
ranging by analyzing source signal and
reflection signal
beepbeep and the fingerIO try to measure
the time of fly by computing the
correlation
llap and strata manage to detect the
phase change cat and RTrack prefers
to use fmcl-based methods
it transforms time delay to frequency
offset
we propose an interesting and
fundamental question for ranging
problems
can deep neural network learn ranging purely based on
raw data we want to input raw samples
of fmcw signal and output range
estimation we have several intuitions
for this proposal
in theory
practically DNN has a huge potential to
achieve higher accuracy it has proved
that dna improve the performance a lot
in computer vision tasks
we can learn from DN structure to
design the diagram of our signal
deep learning to improve the accuracy
our goal is to explore if it is possible
for DNN to learn ranging purely based on
raw samples and if it can outperform the
existing processing. to enable data
driven approach first we need a large
volume of high quality data
however there is no open dataset for
this task
it requires to take much effort to
collect the data considering the
diversity of environments mobile phones
different positions of targeted
reflectors and interference with reflectors
diverse the model can be easily
thus we propose to use realistic
simulation to generate a huge amount of
training and validation data first we
consider the white noise with different
energy bar
then we take account of multiple
interference caused by nearby
reflections
we cannot neglect the direct path
signal comes from mobile phone speaker
to the microphone which is the self
interference
even though we perform the background
cancellation but still add the
residual self interference due to
the imperfect cancellation finally
frequently response is also critical for
simulation it represents an uneven
response scan to different frequencies
as the figure shows the standard fmcw
will have different magnitude at
different frequencies
we use the random spines to represent the
frequency response
we need to design an appropriate neural
network structure to land the hidden
pattern from the simulation data
the chirp time series signal is long
and does not have a clear local pattern
finally we develop a long filter based
CNN to capture the signal features
we start from full connected neural
network which has achieved a promising
accuracy
we figure out that the first layer of fn
has strong correlation to FMCW
signal it motivates us to use a
convolutional neural network which uses
diverse kernels to capture different
local correlations
we use a small kernel from 3 to 11. we
find that larger corner is better and
keeps increasing the kernel size
finally we use a kernel size of 1440
with 4 channels which is the DRNet we
have trained DNN and evaluate performance
on real trace we collect more than 160K
test samples with 11 different
smartphones in different environments
we detect various targets, including
10 user's hands
we add interference by adding other
objects around the target
.
we evaluate our model on the different
the snr is high all the methods can
achieve a low distance error however DRnet
is more robust to the worse snr
it reduce the median error by
a factor of 3
in the worst case we perform thorough
ablation study on the model
we visualize the CNN kernel of four
channels we find that three channels show
similar time frequency property to a
standard fmcw
however they have an uneven energy
distribution one channel distributes
small energy detection pattern
especially as the head and tail of the
sequence
it implies that thin kernels not only
perform trivial fmcw correlation detection
but also show the diversity detect
the uneven pattern which can combat the
noise and interference well. in
concluding we have such contributions
a realistic simulator for ranging
to easily generate large amounts of
training data
the DNN that takes those across the
samples throughout the range we also
provide instance from the DRNet
ablation study
thank you