David Ohm

Electrical and Computer Engineering

Professor of Practice


David Ohm

Contact Information

Email: david.ohm@usu.edu

Educational Background

PhD, Electrical Engineering, Oregon State University, 2007
Kinematic and Cyclostationary Parameter Estimation for Co-Channel Emitter Location Applications
MS, Electrical Engineering, Oregon State University, 2004
Enhanced Inverse Synthetic Aperture Radar Imagery Using 2-D Spectral Estimation
BS, Physics, Oregon State University, 2000
Mapping Magnetic Fields in a Rubidium Vapor Magneto-Optic Trap Using the Hanle Effect

Biography

David Ohm is a Professor of Practice with over 25 years of experience in advanced sensor signal processing, photonics, and defense technology specializing in statistical signal processing for SAR and RF sensing applications. His career in industry includes serving as a Principal Signal Processing Scientist at Zeta Associates (Lockheed Martin) and as Co-Founder and CEO of KickView Corporation, a defense-tech startup focused on RF and EO sensing with AI and machine learning. He has led corporate internship programs and adjunct taught at the University of Colorado Denver and is dedicated to bridging the gap between theory and practice.

Teaching Interests

Dr. Ohm’s teaching philosophy is rooted in first-principles learning, emphasizing a deep understanding of physics and engineering to solve real-world problems with noisy data. His teaching interests center on digital signal processing, communications theory, sensors and sensor processing applications such as radar, perception, emitter geolocation, and RF machine learning. Additionally, he is interested in integrating current technologies and entrepreneurial concepts into the ECE curriculum and hopes to equip students with the technical mastery and creative mindset necessary for fulfilling careers.

Research Interests

Dr. Ohm’s research interests focus on the intersection of statistical signal processing and advanced sensing. His primary areas of investigation include multi-aperture and distributed sensing, detection, estimation and tracking methods for complex signal environments, array processing, direction finding, and sensor fusion, RF machine learning (RFML), RF geolocation, interference mitigation, RF quantum sensing, and novel ISR sensor technologies.

Teaching

ECE 7030 - Detection and Estimation Theory, Fall 2026
ECE 2250 - Electrical Circuits I, Fall 2026
ECE 5660 - Communications Systems I, Spring 2026
ECE 3640 - Discrete-Time Signals & Systems, Spring 2026