Title:
Localization of Subsurface Targets using Optimal Maneuvers of Seismic Sensors
Localization of Subsurface Targets using Optimal Maneuvers of Seismic Sensors
Author(s)
Alam, Mubashir
Advisor(s)
McClellan, James H.
Editor(s)
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Abstract
The use of seismic waves to detect subsurface targets such as
landmines is a very promising technology compared to existing
methods like Ground Penetrating Radar (GPR) and Electromagnetic
Induction (EMI) sensing. The fact that seismic waves induce
resonance in man-made targets, and hence more scattering, gives this
method a natural ability to discriminate landmines from common types
of clutter like rocks, wood, etc. Reflection and resonance from the
targets can be used in imaging to detect the location of targets.
However, existing methods require a large number of measurements for
imaging and detection, which are expensive and time consuming. To
reduce the number of measurements and enable faster detections, a
new sensing strategy is proposed based on optimally maneuvering
sensors. The system would operate in two main modes. In search mode,
the goal would be to move on top of a target using the minimum
number of measurements. Once the target is found, the system would
switch to a detection mode to make its final decision. The seismic
sensor system is an active system, where a seismic source generates
the probing pulse. The waves reflected from buried targets are
collected by an array of sensors placed on the surface, and then an
imaging algorithm is used to estimate the target position. The
performance bounds for this position estimate are derived in terms
of the Fisher information matrix (FIM). This matrix gives the
dependence of the target position estimate on the array position.
Based on the FIM, the next optimal array position is determined by
using the theory of optimal experiments. The next array position
will be the one that reduces the uncertainty of the target position
estimate the most. The whole array is moved to this new position,
where the same steps are repeated. In this way, the target can be
localized in a few iterations.
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Date Issued
2006-05-10
Extent
4080157 bytes
Resource Type
Text
Resource Subtype
Dissertation