Random Set Tracking and Entropy Based Control Applied to Distributed Sensor Networks
May 2007
David Stein, The MITRE Corporation
James Witkoskie, The MITRE Corporation
Stephen Theophanis, The MITRE Corporation
Walter Kuklinski, The MITRE Corporation
ABSTRACT
This paper describes an integrated approach to sensor fusion and resource management applicable to sensor networks.
The sensor fusion and tracking algorithm is based on the theory of random sets. Tracking is herein considered to be the
estimation of parameters in a state space such that for a given target certain components, e.g., position and velocity, are
time varying and other components, e.g., identifying features, are stationary. The fusion algorithm provides at each
time step the posterior probability density function, known as the global density, on the state space, and the control
algorithm identifies the set of sensors that should be used at the next time step in order to minimize, subject to
constraints, an approximation of the expected entropy of the global density. The random set approach to target tracking
models association ambiguity by statistically weighing all possible hypotheses and associations. Computational
complexity is managed by approximating the posterior Global Density using a Gaussian mixture density and using an
approach based on the Kulbach-Leibler metric to limit the number of components in the Gaussian mixture
representation. A closed form approximation of the expected entropy of the global density, expressed as a Gaussian
mixture density, at the next time step for a given set of proposed measurements is developed. Optimal sensor selection
involves a search over subsets of sensors, and the computational complexity of this search is managed by employing the
Mobius transformation. Field and simulated data from a sensor network comprised of multiple range radars, and
acoustic arrays, that measure angle of arrival, are used to demonstrate the approach to sensor fusion and resource
management.
Additional Search Keywords
Tracking, data association, random sets, sensor network control, distributed sensors
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