Nir Regev
Nir Regev is a statistical signal processing, radar signal processing, and artificial intelligence expert, with a career spanning 26 years in algorithm development across various sectors. With a Ph.D. in Electrical Engineering from Ben-Gurion University of the Negev, Israel, he has dedicated his professional life to the exploration and advancement of radar and lidar signal processing, computer vision, machine learning, and AI.
Dr. Regev's expertise encompasses multi-target tracking, radar micro-Doppler phenomena, statistical signal processing, and AI. His role in the industry and academia has been pivotal, guiding projects from conception to completion and disseminating knowledge to peers and students alike.
As a leader of alephzero.ai, Dr. Regev spearheads initiatives that merge theoretical concepts with practical applications. He also serves as an Adjunct Professor in Electrical and Computer Engineering at Cal Poly Pomona, where he is committed to teaching and inspiring the next generation of technologists.
The Micro-Doppler Effect in Radar: Next Generation Remote Medical Applications
Status: Available NowIn the last two and a half decades there has been an increasing body of work on the micro- Doppler effect for various applications. Researchers used micro-Doppler signatures to analyze, classify and detect human gait, hovering helicopters and wind-turbines, as well as jet engine modulation (JEM) to detect jet aircrafts. In recent years, the use of the micro-Doppler effect has expanded and taken also to the monitoring of biological signals. Researchers started investigating the use of the effect to extract vital signs such as breathing and heartbeat. The employment of various algorithms, such as the Chirp Z Transform and Fourier analysis, has been advocated. Multiple radars have been investigated in this context, from UWB and X-Band, to 24, 60, and 77GHz radar bands. The methods developed suffer from insufficient spectral resolution, as Fourier analysis type algorithms need a large time- window of data to support a certain resolution. Another caveat is the fact that the respiration frequency is lower than the heart-beat, while its amplitude is much larger coupled with inherent nonlinearities in the radar hardware, rendering the spectrum of the signal densely populated with harmonics of both the respiration and heartbeat and their inter-modulations. This tutorial will give an introduction to micro-Doppler with the derivation of the micro-Doppler effect of a vibrating target with a live-demo of radar based vital signs extraction from afar.
Live Q&A - The Micro-Doppler Effect in Radar: Next Generation Remote Medical Applications
Status: Available NowLive Q&A with Nir Regev for the talk titled The Micro-Doppler Effect in Radar: Next Generation Remote Medical Applications
AI in Radar Signal Processing
Status: Not yet available - Stay tuned!UPDATE 2024/10/28
We regret to inform you that this session has been postponed due to personal circumstances. We appreciate your understanding and will share any new updates as soon as they become available. Thank you for your patience.
This lecture offers a comprehensive overview of the evolution, principles, and cutting-edge applications of radar technology. We trace radar's development from its early days to modern advancements, emphasizing the integration of digital and statistical signal processing with artificial intelligence (AI). Key topics include the history of radar, modern techniques such as FMCW (Frequency Modulated Continuous Wave) and Pulse Doppler radars, and AI's transformative role in detection, tracking, classification, and decision-making.
We delve into the technical foundations of radar signal processing, explaining concepts like frequency modulation, signal mixing, and range-Doppler processing. The lecture also covers the significant AI applications in radar, such as clutter suppression, target classification, and adaptive waveform optimization. Challenges like the need for large training datasets, model interpretability, and robust AI systems are discussed alongside solutions like data augmentation and generative models.
Through detailed explanations and high-level block diagrams, the lecture aims to provide a solid understanding of radar systems' operational principles and AI's role in enhancing their capabilities. This foundational knowledge prepares participants for deeper exploration of these technologies in subsequent modules.