Areas of Research
- Connected and Autonomous Vehicles
- Machine Learning
- Millimeter Wave Automotive Radar
- Remote Sensing
- Statistical Signal Processing
His research interests lie at the interface of statistical and sparse signal processing with mathematical optimizations, automotive radar, MIMO radar, remote sensing, machine learning, connected and autonomous vehicles.
Examples of on-going projects include:
- High Resolution Imaging Radar System for Level 4 and Level 5 Autonomous Driving
- NOAA/UCAR: Radar Remote Sensing for Snow and Soil Moisture in Greenland and Antarctica
- Deep Neural Networks Based Environment Perception Using Radar Low Level Data
My research lies at the interface of statistical and sparse signal processing with mathematical optimizations, MIMO radar, machine learning with applications emphasis on:
- Radar Signal Processing and Machine Learning for Autonomous Driving and Advanced Driver Assistance Systems (ADAS)
- Radar Remote Sensing for Global Climate Change
- Radio Frequency (RF) Sensing for Digital Health and Smart Home
Current going on projects include
- Waveform Design: FMCW, PMCW, OFDM and Sparse Step Frequency Waveform (SSFW)
- High Resolution Imaging Radar Systems Cascaded with Multiple Radar Transceivers
- Novel Sparse Linear Array Synthesis and Optimization
- Efficient and Effective High Resolution Direction of Arrival (DOA) Estimation Algorithms
- Radar Interference Detection and Elimination (RIDE)
- Novel Machine Learning Methods for Target Identification Using Radar Data
- Sensor Fusion Strategies of Automotive Radar Data and Camera Imaging for Autonomous Driving
