Mutual Information Based Resource Management
Applied to Road Constrained Target Tracking
August 2006
David Stein, The MITRE Corporation
Steve Theophanis, The MITRE Corporation
Walter Kuklinski, The MITRE Corporation
James Witkoskie, The MITRE Corporation
Mike Otero, The MITRE Corporation
ABSTRACT
Netted sensors offer advantages for many surveillance applications. Target tracking and identification
may be enhanced by jointly exploiting a variety of data sources, and certain surveillance applications may
be more readily accomplished with jointly operated small in situ sensors than with large standoff sensors.
Efficiently utilizing sensor-network data requires reliable sensor fusion and resource management
algorithms. Resource management is particularly important when a limited number of tasks can be
performed either because sensors may be used in one of several modes at any given time, or various
resources, e.g. energy, computational capabilities and communication bandwidth are limited. In this
paper tracking is viewed as a parameter estimation problem. Parameters are values in a state space and
inference about the parameters is based on sensor measurements. The utility of sensor measurements is
assessed using the mutual information between the parameters and the measurements. Resource
management is achieved by minimizing average expected entropy subject to constraints. This approach
is applied to a random set tracking algorithm that is based on Gaussian mixture models. Quadratic mutual
information, which in this context is computable in closed form, is used as a substitute for mutual
information when comparing the utility of sets of sensors of the same cardinality. The Mobius
transformation is utilized to reduce the computational requirements of the optimization process. The
tracking and resource management algorithms are demonstrated using a simulation capability. Four
acoustic arrays, that measure angle of arrival and two radars, that measure range, monitor a triangular
road network. For the example shown, two vehicles traversing the network, the tracker and resource
manager are able to maintain the approximate quality of the estimate, as measured by average entropy of
the distribution of the state space parameters, using, on average, less than 2.5 of the six sensors.
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