Dr. Ellick ChanExponent Inc.

Title: Algorithmic Approaches to Detecting Buried Objects in Cluttered Environments

Keywords: Machine Learning, Deep Learning, Radar

Abstract: Detecting and identifying buried objects using ground penetrating radar (GPR) in real-time can be a challenging task due to clutter, uneven ground surfaces, and electromagnetic noise. In this work, we discuss algorithmic approaches to overcoming some of these challenges by using a combination of techniques from machine learning, deep learning, computer vision and signal processing. In particular, we highlight new algorithms to accurately estimate the ground distance under the GPR antenna and radar calibration parameters using machine learning techniques, while under a real-time constraint.We discuss some applications of this work to detect buried explosive hazards in remote environments, such as land mines and improvised explosive devices (IEDs). Our work has achieved state-of-the-art performance such that our algorithms are able to detect targets that even highly trained human spotters can miss. The use of our software with the guidance of a human operator has significantly increased the accuracy of current GPR systems.

Biography: Prior to joining Exponent, Dr. Chan performed post-doctoral research at Stanford on methodologies to prevent de-anonymization of medical record data. For his Ph.D. work, Dr. Chan developed software and algorithms to address many facets of computer security and privacy. His work included development of computer forensic and recovery tools for analyzing live memory dumps of devices operating on critical infrastructures such as power grid monitoring equipment. He also identified security vulnerabilities in embedded microprocessor architectures and operating systems running on them. Dr. Chan has also worked in the software industry on developing malware analysis tools for Windows, Linux-based mobile operating systems; and ARM microprocessor simulation. He has experience with low-level analysis of ARM and X86 machine code, operating system internals, and analysis of software packages.