Dr. Sandeep Gogineni has over 17 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. This work was recognized with the Best Student Paper Award at the 2012 International Waveform Diversity & Design Conference (WDD). At Information Systems Laboratories Inc., Dr. Gogineni has been working as a senior research 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. His expertise includes statistical signal processing, modeling and simulation, detection and estimation theory, machine learning, artificial intelligence, performance analysis, and optimization techniques with applications to active and passive radar systems. 

Dr. Gogineni’s key advances in the field of radar include: 

1. Contributions to MIMO Radar and Waveform Diversity: Prior art in the field was largely based on heuristic and ad-hoc principles. Sandeep’s contributions to the field placed these principles on a firm mathematical pedestal. This provided the radar community with a powerful set of mathematical and analytical tools to bring to bear on vexing basic research challenges of DoD importance. 
2. Contributions to Distributed Radar Performance Bounds: Sandeep’s development of the conditional Cramer Rao bound for distributed passive radar provided the performance limit for Delay-Doppler estimation. In a companion investigation, he developed techniques for illuminator selection on the basis of ambiguity function analysis for distributed radar, which allowed for selection of illuminators that afforded the best ambiguity function. Taken together, the performance bounds and illuminator selection enables passive radar system design.
3. Contributions to Passive Radar Detection: Detection in noisy reference channels is a pressing issue pertaining to passive radars. Sandeep showed that for this case the cross correlator is no longer optimal. Instead, a subspace similarity method was found to be the optimal processing technique for this setting in that it yielded unbeatable detection performance for a fixed false alarm rate. The findings of this research were validated through controlled laboratory measurements. Prior art in this area again relied upon heuristic principles, whereas Sandeep provided a rigorous mathematical contribution validated through simulation and controlled laboratory measurements.
4. Contributions to High Fidelity RF Modeling & Simulation (M&S): Sandeep has been a key contributor in the development of state-of-the-art high-fidelity M&S tools and datasets that are being widely used within the Government, academia, and industry for benchmarking candidate algorithms and techniques.