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Mine Countermeasures Planning
Decision Aid
Daniel H. Wagner Associates developed the Minefield Clearance Effectiveness
Module in the Navy's new Mine Warfare Environmental Decision
Aid Library (MEDAL) package for the Joint Maritime Command Information
System (JMCIS).
The clearance module allows the user to specify which sensors/sweeps
and which mine types to include in the calculations and then
accurately calculates the effectiveness of planned and actual
mine counter-measures (MCM) operations.
As part of this work, we developed the Analytic Bayesian Clearance
Evaluation (ABCE) algorithm, which can very accurately evaluate
the effectiveness of MCM operations in a nonhomogeneous environment.
This algorithm uses an Analytic Bayesian approach to compute
MCM effectiveness, which means that, for each mine of interest,
it analytically divides up the area of interest into non-overlapping
polygonal areas, each of which has a probability of clearance
associated with it.
This picture shows the instantaneous probability of detection
for a Navy BQQ-30 mine hunting sonar.
To get a detection probability coverage map, the instantaneous
probability is converted into a lateral probability curve.
Now we can generate polygonal areas of coverage using the track
of the sensor/sweep, the lateral range curve for the sensor/sweep,
and the estimated ship count distribution for the mine of interest.
The lateral range curve for the sensor/sweep gives the probability
of detection/actuation as a function of the range of the mine
from the sensor/sweep, and can vary throughout the nonhomogeneous
environment in which the area of interest lies.
These features of the ABCE algorithm are particularly important
since MCM operations are conducted in a very nonhomogeneous environment
and modern navigation systems allow the track of the sensor/sweep
to be determined with a high degree of precision.
In contrast, previously developed algorithms for computing the
effectiveness of MCM operations were grid cell oriented, so that
their answers depended heavily on the grid size chosen by the
user, and the accuracy of their answers depended on how well
the grid size matched the data. In addition, earlier algorithms
used an A-B "cookie cutter" detection model rather
than lateral range curves. The A-B detection model sets the probability
of a sensor/sweep detecting/actuating a mine equal to B if the
sensor/sweep comes within a distance of A/2 of the threat mine,
and sets it to zero otherwise.
Since the ABCE algorithm does not depend on choosing a grid and
can compute sensor/sweep effectiveness using lateral range curves,
it can compute a more accurate estimate of the effectiveness
of MCM operations than earlier algorithms.
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