Advanced Physics and Statistics-Based Algorithms for Standoff IED Detection
Technical Report,01 Jul 2016,30 Jun 2019
Michigan Technological University Ann Arbor United States
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Standoff radar systems have the potential to detect buried hazards at safe distances. However, low target-to-clutter ratios and limited spatial resolution resulting from forward-looking measurement geometries limit the performance of automated detection and classification algorithms. The project researched and developed algorithms and measurement approaches to mitigate these limitations. Specifically, the project investigated buried object radar imaging, neural network ATR feature extraction, transfer learning to estimate algorithm extensibility, improvement of buried object imaging using quadratic lifting inversion, and buried object detection and imaging using a ground penetrating radar on a small UAS platform.
- Target Direction, Range and Position Finding
- Miscellaneous Detection and Detectors