Hello, everyone.
My name is Linfei Ge.
Today, I will present “Acoustic Strength-based Motion Tracking”.
Motion tracking is important in smart home scenarios.
For example, the location of smartphone or sweeping robot can help to provide a humanized
interaction.
Nowadays, many smart devices have microphones and speakers in them.
Acoustic-based motion tracking becomes an important research area.
There are already many acoustic-based motion tracking systems.
However, these traditional motion tracking systems are based on distances between microphone
and speaker.
They measure distances and further derive location by using geometry.
Thus, they have to make the array size large to get high accuracy.
Here, we introduce TA ratio, a ratio of Target Distance and Array Size.
In traditional systems, large array size results in small TA ratio.
To improve the TA ratio, we need to reduce the array size.
Can we use angle-based motion tracking, instead of distance-based motion tracking, to reduce
array size?
The answer is “Yes”.
Our work achieves angle tracking by using the acoustic strength of sound field.
Here is an aerial view of a sound field generated by two speakers.
We regard two speakers as point sound sources.
They are at the top end of the figure.
Each of them emits a sine wave.
The bright area has larger sound strength because constructive interference occurs here.
On the contrary, the dark area has smaller sound strength because destructive interference
occurs here.
The reason of uneven strength distribution is that different locations in the sound field
have different distance differences to the two speakers.
The distance difference results in phase difference and strength difference.
Furthermore, if the frequencies of two sine waves are different, there will be a rotating
sound field.
The reason, in simple terms, is that for different frequencies, time will also cause phase difference.
In the rotating sound field, a microphone will sense a changing sound strength.
By using the frequency of the strength change and Doppler Effect, we can achieve angle tracking.
Here is an overview of our system.
The transmitter end controls speakers to emit two sine waves at different frequencies.
In the receiver end, the received signal is filtered by a high-pass filter to remove low-frequency
noise.
Then, angle and distance tracking results are derived respectively.
Finally, combining angle and distance results, motion tracking is achieved.
For the details, there are two transmitted sine waves.
The received signal is the summation of two sine waves.
The figure shows an example of the summation of two sine waves at frequencies of 50 and
55 Hz.
The orange line is the sound strength that we want.
We achieve angle tracking by the frequency of it.
We further get distance by phase-based distance derivation.
Here is the evaluation results of angle and distance estimation.
We achieve accurate angle and distance tracking in the range of 3 m.
However, as the distance increases, the results become worse due to sound attenuation.
The overall motion tracking results are given here.
Our system can reach an accuracy of 5 cm in whether predefined route evaluation or random
route evaluation.
By using angle-based motion tracking, we greatly reduce array size and improve TA ratio.
The speaker separation distance can reach even 1 cm.
The TA ratio is more than 100, which is much larger than traditional distance-based motion
tracking systems.
In conclusion, we have following contributions.
We model the sound field generated by two speakers.
We further propose a novel strength-based angle tracking method.
Based on the method, we design and implement an angle-based motion tracking system.
Here are some motion tracking demos.
We can draw rectangle or circle accurately.
The video shows a motion tracking demo of a smartphone.
Two speakers are at the top of the frame.
The smartphone receives the sound and achieves motion tracking.
Thanks for your watching.