Research Publications
Discover our latest research contributions to the scientific community. Our publications span journal articles, conference papers, and technical reports in radar technology and related fields.
Automotive Radar Interference Mitigation using Adaptive Noise Canceller
F. Jin, S. Cao
A low calculation cost method using adaptive noise canceller to improve signal-to-interference ratio (SIR) for FMCW automotive radar systems. Effectively suppresses mutual interference between multiple radar systems and reduces ghost targets in dense traffic scenarios. Exploits correlation between positive and negative frequency spectrum interference in quadrature receivers.
Multiple Patients Behavior Detection in Real-Time using mmWave Radar and Deep CNNs
F. Jin, R. Zhang, A. Sengupta, S. Cao, S. Hariri, N. K. Agarwal, S. K. Agarwal
A multi-patient behavior detection system using mmWave radar arrays for real-time monitoring in clinical environments. Enables privacy-preserving simultaneous tracking and activity recognition for multiple subjects with deep CNN-based classification. Training dataset includes six types of behavior collected over a long duration.
Real-Time Human Motion Behavior Detection via CNN using mmWave Radar
R. Zhang, S. Cao
A patient behavior detection system using mmWave radar and deep convolutional neural networks (CNN). Supports simultaneous recognition of multiple patients' behaviors in real-time with very good inference accuracy in multi-patient scenarios. System implemented using mmWave sensor on ROS for real-time monitoring.
Support Vector Machines for Classification of Automotive Radar Interference
R. Zhang, S. Cao
Studies classification of automotive radar interference waveforms via support vector machine (SVM). Addresses the challenge of radar-to-radar interference that is inevitable as the number of automotive radars increases in advanced driver assistance systems (ADAS) for road traffic safety.
Transform-Sensing Array Based on Wavelets
S. Cao, D. Brendel, Y. F. Zheng, R. L. Ewing
Proposes transform sensing to sense space through beam patterns designed for a transformation basis, where received signals are the result of transformation rather than original raw data. Uses wavelets for transform-sensing mechanism. Results show that transform sensing obtains high-resolution data on target areas while spending less time on nontarget ones, generating a multiresolution result that balances resolution and sensing efficiency.
Slow-time waveform design for MIMO GMTI radar using CAZAC sequences
S. Cao, N. Madsen
Proposes slow-time waveform design for MIMO GMTI radar using CAZAC (Constant Amplitude Zero Auto-Correlation) sequences. The approach improves ground moving target indication performance through optimized waveform selection for MIMO radar systems.