Synthetic Aperture Radar for Mine Detection and Identification.
Abstract
This project has focused on modeling and signal processing for the detection and identification of buried and surface land mines, both metal and plastic. The modeling has been performed through development of a method of moments (MoM) for general conducting/dielectric targets in an arbitrary multi-layered environment. The model accounts for all loss and dispersion associated with real soils. Using the MoM models, we have generated computed synthetic-aperture radar (SAR) imagery for buried and surface mines, with this model data compared very favorably to data measured by the Army Research Laboratory (ARL). Moreover, the models have been employed in an optimal Bayesian processor, in which the real-world uncertainties have been accounted for, including variability in the soil properties and the target depth. For the case of anti-tank mines, the results of the Bayesian processor are very encouraging, demonstrating a dramatic decrease in the false alarm rate, via-a-vis traditional approaches. This suggests that SAR may be a viable technology for mine field detection.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 31, 1999
- Accession Number
- ADA370415
Entities
People
- Lawrence Carin
Organizations
- Duke University