KEYWORDS: Visualization, Sensors, 3D visualizations, LIDAR, Surveillance, Situational awareness sensors, Network architectures, Communication engineering, Environmental sensing, 3D modeling, Clouds, 3D image processing, Data modeling, Image compression, Visual process modeling, 3D image reconstruction, Reconstruction algorithms
We report progress toward the development of a compression schema suitable for use in the Army’s Common Operating Environment (COE) tactical network. The COE facilitates the dissemination of information across all Warfighter echelons through the establishment of data standards and networking methods that coordinate the readout and control of a multitude of sensors in a common operating environment. When integrated with a robust geospatial mapping functionality, the COE enables force tracking, remote surveillance, and heightened situational awareness to Soldiers at the tactical level. Our work establishes a point cloud compression algorithm through image-based deconstruction and photogrammetric reconstruction of three-dimensional (3D) data that is suitable for dissimination within the COE. An open source visualization toolkit was used to deconstruct 3D point cloud models based on ground mobile light detection and ranging (LiDAR) into a series of images and associated metadata that can be easily transmitted on a tactical network. Stereo photogrammetric reconstruction is then conducted on the received image stream to reveal the transmitted 3D model. The reported method boasts nominal compression ratios typically on the order of 250 while retaining tactical information and accurate georegistration. Our work advances the scope of persistent intelligence, surveillance, and reconnaissance through the development of 3D visualization and data compression techniques relevant to the tactical operations environment.
Our research focuses on the Army's need for improved detection and characterization of targets beneath the
forest canopy. By investigating the integration of canopy characteristics with emerging remote data collection
methods, foliage penetration-based target detection can be greatly improved. The objective of our research was
to empirically model the effect of pulse return frequency (PRF) and flight heading/orientation on the success of
foliage penetration (FOPEN) from LIDAR airborne sensors. By quantifying canopy structure and understory
light we were able to improve our predictions of the best possible airborne observation parameters (required
sensing modalities and geometries) for foliage penetration. Variations in canopy openness profoundly influenced
light patterns at the forest floor. Sunfleck patterns (brief periods of direct light) are analogous to potential
"LIDAR flecks" that reach the forest floor, creating a heterogeneous environment in the understory. This
research expounds on knowledge of canopy-specific characteristics to influence flight geometries for prediction of
the most efficient foliage penetrating orientation and heading of an airborne sensor.
Herein we purpose to utilize upconverting phosphors to detect explosives. To detect TNT, antibodies specific
to TNT are conjugated to the surface. The role of the antibodies is two fold; to bind a quencher and to accept
TNT. The quencher is a bifunctional molecule, with one end containing a TNT analog and the other end being
a dark fluorescent quenching dye. The dye is chosen so that the luminescence from the phosphor will be
absorbed preventing it from emitting, reducing luminescence from the phosphor. However, in the presence of
TNT the quencher that is bound with DNT will be displaced. With the quencher displaced the phosphor will be
able to emit light indicating TNT is present in the select area.
Conference Committee Involvement (1)
Geospatial Informatics XIV
25 April 2024 | National Harbor, Maryland, United States
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