Seeking algorithms that enable the detection of a broad range of threat targets. Detection performance is often reliant on the ability to discriminate targets amongst varying levels of clutter, obscurants, or decoys. In many cases, a single sensing modality is inadequate for high-confidence detection. The U.S. Army is interested in identifying approaches to fusing data from multiple types of sensors and disparate platforms, for example, a wide field-of-view electro-optic imager with a synthetic aperture radar. The primary targets of interest for this effort include surface and buried landmines, located in natural and urban environments.
Further details and descriptions will be provided when we are able to meet with our sponsor from the DoD.
| Folder | Description |
|---|---|
| Documentation | all documentation the project team has created to describe the architecture, design, installation, and configuration of the project |
| Notes and Research | Relevant helpful information to understand the tools and techniques used in the project |
| Project Deliverables | Folder that contains final pdf versions of all Fall and Spring Major Deliverables |
| Status Reports | Project management documentation - weekly reports, milestones, etc. |
| scr | Source code - create as many subdirectories as needed |
Note: Commits behind this fork could be automatically synced, meaning that changes made in the template are pushed into your repo. Please do not discard commits ahead (these are the updates you make to this repository).
- Nibir Dhar - DoD - Mentor
- James Perea - DoD ASPIRE - Technical Advisor
- Changqing Luo - CS - Faculty Advisor
- Jeffrey Weaver - CS - Student Team Member
- Paul Reid - CS - Student Team Member
- Grace Gillam - CS - Student Team Member
- David Anthony - CS - Student Team Member