Stochastic Systems Group  

Prof. Randy Moses
Ohio State University
We consider the problem of locating and orienting a network of unattended sensors that have been deployed in a scene at unknown locations and orientation angles. This selflocalization problem is solved by placing a number of source signals, also with unknown locations, in the scene. A subset of sensors in the network measures the timeofarrival, and possibly the directionofarrival with respect to the sensor's local orientation, of the signal emitted from each source. From these measurements we compute the sensor locations and orientations, along with any unknown source locations and emission times. We develop necessary conditions for solving the selfcalibration problem and provide a maximum likelihood solution and corresponding location error estimate. We compute the CramerRao Bound of both the relative and absolute sensor location and orientation estimates. Finally, we consider the case when no "anchor" nodes are present, and consider maximum a posteriori estimation algorithms and associated performance bounds. Results using both synthetic data and field measurements are presented.
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