MMWave Reflections for Object Detection
Session Number
Project ID: CMPS 40
Advisor(s)
Zihan Zhou
Matthew Caesar, University of Illinois Champaign Urbana
Discipline
Computer Science
Start Date
17-4-2024 8:55 AM
End Date
17-4-2024 9:10 AM
Abstract
This project explores the potential applications of millimeter-wave (mmWave) radar technology for object and activity recognition. Using a Texas Instruments radar wave card and several cases, the data retrieved demonstrates the capability of mmWave radar to distinguish between different objects. Mmwave is unique in the sense that it uses the reflection points of wifi-waves which are more adverse in their nature to collect data. The methodology feeding this into neural network models to interpret the unique "signatures" of various subjects. By analyzing the point cloud data generated by mmWave radar, which includes spatial coordinates, velocity, range, intensity, and bearing angle algorithms capable of recognizing eleven distinct object classes and several human activities, future research can be done for advanced applications in cyber-security, through device and keyboard recognition, and in automotive and pedestrian safety, by accurately identifying and tracking moving objects. MmWave radar technology is unique in its ability to penetrate adverse weather conditions and other obstacles yet still offer detailed spatial information, which presents a promising alternative to conventional imaging and sensing methods like other waves such as X- ray, ultrasonic, or magnetic.
MMWave Reflections for Object Detection
This project explores the potential applications of millimeter-wave (mmWave) radar technology for object and activity recognition. Using a Texas Instruments radar wave card and several cases, the data retrieved demonstrates the capability of mmWave radar to distinguish between different objects. Mmwave is unique in the sense that it uses the reflection points of wifi-waves which are more adverse in their nature to collect data. The methodology feeding this into neural network models to interpret the unique "signatures" of various subjects. By analyzing the point cloud data generated by mmWave radar, which includes spatial coordinates, velocity, range, intensity, and bearing angle algorithms capable of recognizing eleven distinct object classes and several human activities, future research can be done for advanced applications in cyber-security, through device and keyboard recognition, and in automotive and pedestrian safety, by accurately identifying and tracking moving objects. MmWave radar technology is unique in its ability to penetrate adverse weather conditions and other obstacles yet still offer detailed spatial information, which presents a promising alternative to conventional imaging and sensing methods like other waves such as X- ray, ultrasonic, or magnetic.