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Active Information Fusion |
Efficient Sensor Selection Via Submodularity and Patitioning
Given a sensor selection criterion,
it is still necessary to have effienct sensor selection algorithms. We propose
efficient sensor selection algorithms by exploiting both the characteristics of the
utility function and the probabilistic dependency among the sensors for two
typical cases: the budget-limit case and the optimal-tradeoff case. The
budget-limit case involves selecting a sensor set that provides the maximum
information gain within a budget limit. The optimal-tradeoff case
involves selecting a sensor set that optimizes the tradeoff between
information gain and cost. We first prove that mutual information
is a submodular function in a relaxed condition, which helps the
proposed algorithms to have performance guarantees. For the budget-limit case,
we introduce a greedy approach that has a
constant factor of (1-1/e) guarantee to the optimal performance. To further improve the
computation, we introduce a partitioning procedure that exploits the probabilistic
independence among the sensors. For the optimal-tradeoff
case, a submodular-supermodular procedure is embedded with the
proposed sensor selection algorithm to choose the sensor set that achieves
the optimal tradeoff between the benefit and the cost.
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