The sensor selection problem for bounded uncertainty sensing models
Isler, V.; Bajcsy, R.
Information Processing in Sensor Networks, 2005. IPSN 2005. Fourth International Symposium on
Volume , Issue , 15 April 2005 Page(s): 151 - 158
Digital Object Identifier   10.1109/IPSN.2005.1440917
Summary: We address the problem of selecting sensors so as to minimize the error in estimating the position of a target. We consider a generic sensor model where the measurements can be interpreted as polygonal, convex subsets of the plane. This model applies to a large class of sensors including cameras. We present an approximation algorithm which guarantees that the resulting error in estimation is within a factor 2 of the least possible error. In establishing this result, we formally prove that a constant number of sensors suffice for a good estimate-an observation made by many researchers. In the second part of the paper, we study the scenario where the target's position is given by an uncertainty region and present algorithms for both probabilistic and online versions of this problem.

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