Siyang Cao

The University of Arizona

My research is motived by the challenge of building commercial radar system which is able to generate high resolution sensing result under complex environment. I am interested in system level design of radar, and the related signal processing technologies to improve the overall radar performance.

Some particular problems of current interest are

radar imaging technique
radar interference mitigation
radar target signature
sensor fusion between radar and camera

Human Behavior Classification

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients’ behaviors in real-time, is proposed. The system was tested for real-time operation and obtained a very good inference accuracy when predicting each patient’s behavior in a two-patient scenario.

Papers

R. Zhang and S. Cao, “Real-time Human Behavior Detection via CNN using mmWave Radar,” IEEE Sensors Letters, vol. 3, no. 2., pp. 1-4, 2019.
F. Jin, R. Zhang, A. Sengupta, S. Cao, S. Hariri, N. Agarwal, and S. Agarwal, “Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs," in Proc. IEEE Radar Conference, April 2019.

Applied in SeVA Technology LLC

https://www.sevatec-llc.com/wp-content/uploads/2019/04/SeVA-Scenario-1-Patient-Waiving-Hand-for-Help.mp4

Automotive Radar Interference Mitigation

Interference among frequency modulated continues wave (FMCW) automotive radars can either increase the noise floor, which occurs in the most cases, or generate a ghost target in rare situations. To address the increment of noise floor due to interference, we proposed a low calculation cost method using adaptive noise canceller to increase the signal-to-interference ratio (SIR). As a result, both the simulation and experiment showed a good interference mitigation performance.

Papers (related patent submitted)

F. Jin and S. Cao, “Automotive Radar Interference Mitigation using Adaptive Noise Canceller," IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp.3747-3754, 2019.

Radar Imaging Technique

The recent effort shows that the movement of a single transceiver radar system can lead to a 3D imaging. A portable millimeter wave radar 3D imaging system is proposed. The range-doppler information can be extracted from the radar raw data, and a further inverse Radon transform can convert the relative velocity projection data and angle vectors into the linear Cartesian coordinators, i.e. azimuth and elevation.

Papers

R. Zhang and S. Cao, “Compressed Sensing for Portable Millimeter Wave 3D Imaging Radar,” in Proc. IEEE Radar Conference, May 2017.
R. Zhang and S. Cao, “Portable Millimeter Wave 3D Imaging Radar,” in Proc. IEEE Radar Conference, May 2017.

Sensor Fusion between Radar and Camera

No single sensor system has the ability to provide decision support, such as response to situation, target identification, simultaneously. The challenges to full autonomy further include synchronization of information (data) from various sensors, and at-scale and rapid computing to ensure stable operation for classifying targets with high fidelity. We are working on utilizing high resolution data from mmWave radar sensors with deep neural networks, coupled with transformation matrix (TM) computation using vision sensor data, to further enhance mmWave radar capabilities, with the end goal to demonstrate detected pedestrian and vehicles.

Research Support

I gratefully acknowledge ongoing and past support from sponsors for my research group.