Due to an object's unique combination of several physical characteristics, including conductivity and permeability, detection systems using the principles of electromagnetic induction (EMI) can be used to detect and classify a characteristic shape or signature for various targets. Subsequently, a library of signatures for targets of interest may be generated for classification purposes to reduce the false alarm rate associated with remediation. However, targets of interest are rarely isolated in the subsurface environment; metallic clutter and/or other targets of interest may be located in close proximity, thereby altering the returned signature and creating the possibility of false alarms. In this paper, we will present data that was collected on UXO and mine-like targets located in close proximity to each other using a prototype frequency-domain EMI sensor, the Geophex GEM-3. We will show that weighted combinations of each object's independent signature can represent the resulting EMI response from two objects, located in close proximity. We will also present simple algorithms to detect the presence of overlapping objects and analyze their performance as a function of object separation and other relevant parameters. The creation and use of target recognition algorithms that consider multiple closely spaced objects will also be discussed.
Sensor data fusion;Ordnance;Object recognition;Mines;Blind source separation;Frequency domain analysis;Independent component analysis;Matrix algebra;Algorithms;