Dr. Yonel of the Department of Electrical and Computer Engineering at the University at Albany, SUNY

A challenging problem in radar imaging is the localization and tracking of moving targets with high resolution. Imaging modalities such as synthetic aperture radar use the relative motion of the antenna with respect to a stationary scene to generate high-resolution imagery via conventional Fourier inversion techniques applied over a coherent processing interval. The presence of moving targets and dynamics in the scene of interest violates the core assumptions of synthetic aperture formation, introducing major artifacts and potential limitations in the resolution of synthetic aperture imaging systems. Motion in the scene introduces spatially varying, velocity-dependent range variations which must be corrected for accurate parameter estimation, image refocusing, and tracking of key targets of interest. Furthermore, conventional radar systems are designed to optimize resolution in range, and to exhibit Doppler tolerance for optimal target detection in lieu of high Doppler/velocity resolution, where the two correspond to a fundamental trade-off in waveform design under the classical formulations. 

In this talk, we will discuss novel approaches towards super-resolution in ground moving target imaging (GMTI). We will first introduce a convex optimization-based approach that leverages a natural low-rank+sparse decomposition on the phase-space reflectivity domain, which characterizes location and velocity parameters for the targets in the scene in a lifted, higher dimensional parameter space to admit a structured linear inverse problem. We analyze the capabilities and limitations of this method, which performs simultaneous stationary background recovery along with the recovery of ground moving targets. We next motivate a novel super-resolution technique for GMTI based on the atomic norm minimization (ANM) framework that reformulates the off-grid recovery of sparse moving target parameters as a semi-definite program that is tractable per slow-time/antenna-location at sub-resolution cell precision levels. To this end, we use ANM to decompose the received SAR data into the echoes received from each scatterer in the scene, which facilitates processing echoes individually across the slow-time samples. We then use the estimates from the ANM decomposition for simultaneous localization and 2D velocity estimation by formulating a quadratic inverse problem, which is solved by a Gauss-Newton method. We derive new and novel trade-offs between SAR imaging parameters and the number of targets resolved under the separation conditions of ANM theory, and establish the conditions required on the antenna trajectory for the performance guarantees of our method. We finally demonstrate the off-grid, sub-resolution cell localization and 2D velocity estimation capability of our SAR-GMTI method via numerical simulations, with comparisons to grid-based approaches and robustness evaluations in the presence of additive noise, clutter, and trajectory errors. We finally discuss future directions towards extensions to problems such as passive source localization, and towards handling low signal-to-clutter ratio conditions.

Biography:

Dr. Bariscan Yonel is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany, SUNY. His research focuses on optimization theory, inverse problems in imaging, and statistical signal processing, with an emphasis on developing imaging methods with provable performance guarantees. His work leverages convex and nonconvex optimization and low-rank matrix recovery theory to characterize and improve resolution, stability, and robustness in challenging sensing environments, particularly for multi-static radar and synthetic aperture radar systems. Dr. Yonel is a recipient of the 2026 ORAU Ralph E. Powe Award for early career faculty.