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Sensor Selection in Arbitrary Dimensions
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DOI：10.1109/TASE.2008.917096.png)
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摘要
En 中文
We address the sensor selection problem which arises in tracking and localization applications. In sensor selection, the goal is to select a small number of sensors whose measurements provide a good estimate of a target's state (such as location). We focus on the bounded uncertainty sensing model where the target is a point in the d-dimensional Euclidean space. Each sensor measurement corresponds to a convex polyhedral subset of the space. The measurements are merged by intersecting corresponding sets. We show that, on the plane, four sensors are sufficient (and sometimes necessary) to obtain an estimate whose area is at most twice the area of the best possible estimate (obtained by intersecting all measurements). We also extend this result to arbitrary dimensions and show that a constant number of sensors suffice for a constant factor approximation in arbitrary dimensions. Both constants depend on the dimensionality of the space but are independent of the total number of sensors in the network.
Keyword:
Camera networks and sensor selection
computational geometry and object modeling
geometric algorithms
languages
and systems
minimum enclosing simplex
polytope approximation
sensor networks
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6.4
论文数:
5.0K
被引数:
1.6W
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Collaborative signal and information processing: An information-directed approach.png)
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