Radio Frequency (RF) signals are used in a multitude of defense, commercial, and civilian applications, which are critical for the safety and security of mankind. In radar systems, an RF transmitter sends out signals to illuminate a surveilled scenario for gathering information about the environment and the targets present based on the received radar echo. However, in practice, the radar returns at the receiver are almost
always corrupted by interfering signals. A major source of interference is reflections from ground clutter, which are highly dependent on the site-specific environment. The development of any new radar signal processing technique is heavily dependent on accurately modeling the ground clutter reflections. However, there is a scarcity of publicly available measured data for RF applications. Therefore, most of the radar
research, development, and testing relies upon accurately modeling and simulating the data. In this lecture, a Green’s function impulse response (stochastic transfer function) approach to radar clutter modeling will be presented along with a comparison to traditional approximate statistical modeling. This alternate approach enables high-fidelity site-specific physics-based clutter modeling to generate representative synthetic data. Various RF applications will be demonstrated using this approach along with the dissemination of a new challenge dataset that can be downloaded to test and benchmark state-of-the-art cognitive radar algorithms and techniques.

Dr. Sandeep Gogineni has over 18 years of experience working on radar and wireless communications systems. He worked for 6 years
as an on-site contractor for Air Force Research Laboratory (AFRL), developing novel signal processing algorithms and performance
analysis for passive radar systems. He received the 2018 IEEE Dayton Section Aerospace and Electronics Systems Society Award
for these contributions to passive radar signal processing. Prior to his time at AFRL, during his graduate studies at Washington University
in St. Louis, Dr. Gogineni developed optimal waveform design techniques for adaptive MIMO radar systems and demonstrated
improved target detection and estimation performance. At Information Systems Laboratories Inc., Dr. Gogineni has been working as a
senior scientist on developing state-of-the-art high-fidelity RF modeling and simulation tools, channel estimation algorithms and
optimal probing strategies for MIMO radar systems in the context of Cognitive Fully Adaptive Radar (CoFAR). He has also developed
AI/ML based solutions for complex RF applications and implemented them on low C-SWaP neuromorphic hardware.