Automotive radar target detection using artificial intelligence techniques

• Main Author: Ng, Wei Chong
• Other Authors: Lin Zhiping
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2020
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/139449
• Institution: Nanyang Technological University

This report presents a comprehensive study on radar detection using deep learning with application to automotive vehicles. Automotive radars face complex target scenarios consisting of both small point targets and large extended targets. However, the current works on automotive radar detection mainly focus on point target detection. Moreover, those works use the complex range-Doppler data for detection. In this report, a deep learning-based method for extended target detection was presented that takes advantage of augmented data for neural network training and prediction. Extensive simulations had been conducted to evaluate the proposed detection method and the results show performance improvement over a recent related method. A paper was submitted and had been accepted in The International Joint Conference on Neural Networks (IJCNN), July 2020.