In this paper, we investigate data fusion techniques for target tracking
using distributed sensors. Specifically, we are interested in in how
pairs of bearing or range sensors can be best assigned to targets in order
minimize the expected error in the estimates. We refer to this as the focus
of attention (FOA) problem.
In its general form, FOA is NP-hard and not well approximable. However,
for specific geometries we obtain significant approximation results: a 2-approximation
algorithm for stereo cameras on a line, a PTAS for when the cameras are equidistant,
and a 1.42 approximation for equally spaced range sensors on a circle. By
reposing as a maximization problem - where the goal is to maximize the number
of tracks with bounded error - we are able to leverage results from maximum
set-packing to render the problem approximable. We demonstrate the
results in simulation for a target tracking task, and for localizing a team
of mobile agents in a sensor network. These results provide insights
into sensor/target assignment strategies, as well as sensor placement in
a distributed network.
@INPROCEEDINGS{ISKT:03,
AUTHOR = {V. Isler, J. Spletzer, S. Khanna, and C.J. Taylor},
TITLE = {Target Tracking in Sensor Networks: the Focus of Attention Problem},
YEAR = {2003},
BOOKTITLE = {Proceedings of the Conference on Intelligent Robots and Systems (IROS 2003)},
ADDDRESS = {Las Vegas, USA}
}