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This paper develops a non-model based vehicle tracking methodology for extracting road user trajectories as they pass through the field of view of a 3D LiDAR sensor mounted on the side of the road. To minimize the errors, our work breaks from conventional practice and postpones target segmentation until after collecting LiDAR returns over many scans. Specifically, our method excludes all non-vehicle returns in each scan and retains the ungrouped vehicle returns. These vehicle returns are stored over time in a spatiotemporal stack (ST stack) and we develop a vehicle motion estimation framework to cluster the returns from the ST stack into distinct vehicles and extract their trajectories. This processing includes removing the impacts of the target's changing orientation relative to the LiDAR sensor while separately taking care to preserve the crisp transition to/from a stop that would normally be washed out by conventional data smoothing or filtering. This proof of concept study develops the methodology using a single LiDAR sensor, thus, limiting the surveillance region to the effective range of the given sensor. It should be clear from the presentation that, provided sufficient georeferencing, the surveillance region can be extended indefinitely by deploying multiple LiDAR sensors with overlapping fields of view. 

Ultrawideband (UWB) radar sensors are an emerging biosensing modality that can be used to assess the dielectric properties of internal tissues. Antenna effects, including antenna body interactions limit the sensors ability to isolate the weak returns from the internal tissues. In this paper we develop a data driven calibration method for recovering Green’s function of the multilayered media model of the tissue profiles using an Invertible Neural Network (INN). The proposed INN structure is trained to invert the antenna transfer function to form estimates of the Green’s function modeling returns from internal tissues. We use simulation experiments to assess the effectiveness of the trained INN in antenna transfer function inversion.