Isolating and Discriminating Overlapping Signatures in Cluttered Environments
Electromagnetic induction (EMI) data from subsurface targets must be processed and inverted in order to discriminate innocuous scrap metal or geology from UXO. At highly contaminated sites, processing data containing overlapping responses from unexploded ordnance (UXO) and clutter is a difficult task both theoretically and computationally. The total EMI response from these densely populated sites is a combination of responses from single or multiple UXO and obscuring clutter resulting in confused or confounded inversion routines. This data contamination often leads to misclassification at best, or a complete failure of the routine at worst.
The objective of this project is to mitigate the effects of dispersed metallic clutter, to resolve the contribution of independent or coupled objects to the composite EMI data, and to discriminate each object of interest based on its response under more realistic field conditions.
Specific objectives include:
- Develop an N-target locater that, without any computationally expensive optimizations, provides an estimate of the number of objects within a sensor’s field of view and their locations and orientations. A spatial particle filter and a formulation of the HAP method for multiple targets will be investigated.
- Formulate robust classifiers that segregate N objects into UXO and non-UXO, based on their isolated EMI responses.
- Discriminate UXO-like targets using rigorous models that explicitly include coupling between targets if required (Normalized Surface Magnetic Source [NSMS] and Standardized Excitations Approach [SEA]).
This research addresses utilizing EMI data acquired under more realistic field conditions where clutter is of paramount importance and more than one target of interest may be in the field of view of the sensor. After mitigating clutter responses and locating each significant object, discrimination routines based on complete, rigorous models will be applied to the response from each target. High-quality, well-located vector data from state-of-the-art sensors developed under SERDP projects MR-1443 (MPV), MR-200601 (TEMTADS), MR-200437 (BUD), and MR-1537 (GEM-3D+) will facilitate this research, providing an unmatched level of data quality and diversity.
Methods and processing schemes resulting from this research will be applicable to any sensor, either in the frequency or time domain. The tools developed will aid efforts to reject unwanted clutter responses from EMI data and will provide convenient and fast tools to locate multiple objects simultaneously. More accurate discrimination capabilities will be developed through these methods, especially in more realistic field conditions, leading to lower false alarm rates and lower overall remediation costs.
Points of Contact
Dr. Ben Barrowes
U.S. Army Engineer Research and Development Center (ERDC)
SERDP and ESTCP
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