Hello, everyone. This is Binbin Xie, a PhD student from UMass Amherst.
I‚Äôm going to present our work: Pushing the Limits of Long Range Wireless Sensing
with LoRa
Wireless sensing has been a hot research topic in the last few years and many exciting applications
have been enabled such as passive localization, health care, and HCI.
One key problem is the range of wireless sensing is limited.
For example, the current acoustic sensing range is around 2 m.
For RFID, the sensing range is around 5m. Even for WiFi, the range is still less than
10 m. In this work, we aim to increase the sensing
range to around 100m with LoRa.
Now let me briefly introduce the LoRa basics. LoRa is recently designed for long range communications
for IoT devices. LoRa can receive weak signals even 20 dB below
the noise Ô¨Çoor because of Chirp Spread Spectrum modulation.
Thus, LoRa has a km-level communication range and a strong penetration capability.
Therefore, we believe LoRa has a great potential in increasing the sensing range.
Now let me show the system overview. First, before we apply the proposed methods,
we perform the signal ratio operation to remove the random phase offset caused by unsynchronized
transmitter and receiver. Then our methods contain two parts.
The first part is enlarging the movement-induced phase variation.
The second part is time-domain beamforming method.
Both these two parts contribute to a long sensing range.
Now we use the walking sensing as one example to illustrate our concept.
In this scenario, there are multiple signals arriving at the receiver.
We group the signal paths into two groups. The first group is static path.
In the vector space, we use the vector Vs to represent it.
The second group is the dynamic signal path. We use Vd to represent the dynamic signal.
Vc is the sum of dynamic and static vectors, which is called composite vector.
During the human walking, the static vector does not change, and the dynamic vector rotates
with respect to the static vector. Then, the rotation can induce a phase variation.
If the target is far away from the LoRa devices, the phase variation is quite small and can
be easily buried in the noise.
So to achieve a long sensing range, we need to enlarge the phase variation.
And we find that the amplitude of static vector affects the induced phase variation.
As we can see from the figures, the amplitude of static vector Vs is different at three
different scenarios. The dynamic vector is the same.
The corresponding induced phase variations are different.
When the amplitude of static vector is smaller than that of dynamic vector, the induced phase
variation is the largest A larger phase variation means a better sensing
performance.
So how to tune the amplitude of static vector to make it smaller?
To do this, we propose a signal processing method.
As we can see from the figure, originally the phase variation is small.
Now we add a signal vector Vadd to the original static signal Vs.
Then, the new static vector Vsnew has a smaller amplitude.
In this case, the phase variation is much larger.
In the previous slides, we already show how to enlarge the phase variation for large-scale
movement. For small-scale movement such as respiration,
the factors and signal processing are different. Please refer to our paper if you are interested.
After enlarging the phase variation, we adopt a time-domain beamforming method to further
increase the sensing range. Our key idea is to sub-sample the received
signals in the time domain. These subsamples contain the same target movement,
but different noise. Then, by aligning and adding these sub-samples,
we can make the phase variations constructively combine, and the noise randomly combine.
Next let‚Äôs talk about our evaluation. We conduct experiments for respiration sensing
in a hallway, and carry out experiments for walking sensing
in a football field.
Experiment results show that the sensing range of our system for human walking is 120 m
We are also able to accurately sense human respiration at a distance of 75 m.
Our methods also enable accurate respiration sensing through 7 concrete walls.
In summary, our proposed method can significantly improve the sensing range by 140%.
We believe the proposed system can be utilized for disaster recue.
That‚Äôs all for presenting our work. Thank you for your attention.