Hello everyone. I’m Minghui from WCSNG at UC San Diego. Today I will present our work
titled: ULoc: Low-Power, Scalable and cm-Accurate UWB-Tag Localization and Tracking for Indoor
Applications.
Remember how the GPS on your phone changed all our lives since the smartphone era? In
the IoT era, indoor localization will be the next GPS
What are the current technologies to enable indoor localization?
While there are various solutions out there like visual and RF based systems, none of
them meet all these requirements other than UWB with its unique high bandwidth and thus
has been widely adapted in consumer market and industry. But, are these the only requirements
that UWB has to meet?
To better understand what today’s applications demand, let’s place ourselves in a huge
smart warehouse, with hundreds of boxes, robots, and employees working in close proximity.
In this warehouse, we want the robots to find, navigate to, then pick up items on their own,
and not to occasionally catch human workers off guard. How can we make this warehouse
smart while preventing such accidents?
This requires the infrastructure to localize these 100s of assets within an instant and
with high 3D accuracy. A simple solution is to attach long-lasting
UWB tags to people and assets, but can the current UWB system satisfy these requirements?
Current systems commonly employ two-way-ranging (TWR), to provide the location of the tag.
In TWR, at least two packets, a poll and a response, are exchanged between each anchor
and tag. Next, these packets exchanges happen with
each anchor in the environment. There will be at least 8 packet exchanges to furnish
a single 3D location. Hence, TWR systems not only have high latency,
as they exchange so many packets, they burn up a lot of power on the tag and make it hard
to scale up Clearly, TWR cannot meet the requirements
we talked about.
In contrast, we present ULoc where we designed a tiny tag to have a very simple protocol
that makes it low latency, allows it to conserve energy, and leaves the rest to the infrastructure.
Then we designed an anchor to work with such tag protocol. It makes the locations of each
tag centralized at the infrastructure while remaining scalable, and has no compromise
in accuracy.
Now let me go into the details on how we come up with these designs. Surprisingly what we
first learned is that UWB reception is at least 50% more power-hungry than transmission.
Contrast to traditional wireless radios where power amplifiers make transmissions power-hungry,
wideband cross-correlation for packet identification makes UWB reception more power-consuming.
Some previous works have the tag turned on in continuous reception mode, and by actively
searching for UWB packets and computing its own location, it consumes up to 10 times more
power than transmitting. Imagine the tag which can last for three years only lasting for
one month.
Further, tag locating itself makes it require bulky hardware. While this is great for smartphone
types of devices, it will not fit our requirements.
Based on these observations, we designed the simplest tag where it wakes up and transmits
a beacon, and then goes back to sleep. This leaves the complexity onto the anchor to make
sure it localizes this tag in 3D in low latency and high accuracy.
So, how do we design our anchors to handle this?
Turns out if we can measure the angle of arrival of that beacon to multiple anchors, we can
triangulate the location of the tag.
Thus ULoc for the first time designed its anchor to enable precise 3D angle-of-arrival
measurement. With a single beacon from the tag, it can output the angle of the tag in
polar and azimuth plane.
Now what's in this black-box anchor that estimates angle-of-arrival? We custom-designed the ULoc
anchor boards using eight UWB Transceivers, carefully spaced out in an “L-shape”.
More details on how we synchronize these 8-antennas and perform 3D AoA estimations can be found
in our paper and the 20min talk.
This however, is not the end of problem. we observed that in dynamic multipath-rich conditions
ULoc anchor can make incorrect predictions on the angle-of-arrivals. To solve this, we
developed a tracking algorithm further details of which can be found in our 20min talk and
our paper.
Now to see ULoc in action, we track ULoc tag collocated with a VR controller held by the
user. The bottom left shows VR controller ground truth in blue and ULoc prediction in
orange. We can see that ULoc gives only an 8cm of median error and 18 cm of 90th percentile
error in 3D.
To summarize, by using single beaconing protocol paired up with our custom-designed 3D AoA
measuring anchor and tracking algorithm, we have made ULoc an indoor localization and
tracking system that requires low power, remains scalable in various setups, and outputs with
low latency cm-accurate 3D locations.
ULoc’s hardware design and software are open source. If you have any questions, feel
free to reach out any time!