The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Computing the exact maximum likelihood or maximum a posteriori estimate of the environment is computationally expensive in many practical distributed sensing settings. We argue that this computational difficulty can be over- come by increasing the number of sensor measurements. Based on our work on the connection between error cor- recting codes and sensor networks, we propose a new algo- rithm which extends the idea of sequential decoding used to decode convolutional codes to estimation in a sensor net- work. In a simulated distributed sensing application, this algorithm provides accurate estimates at a modest compu- tational cost given a sufficient number of sensor measure- ments. Above a certain number of sensor measurements this algorithm exhibits a sharp transition in the number of steps it requires in order to converge, leading to the poten- tially counter-intuitive observation that the computational burden of estimation can be reduced by taking additional sensor measurements.

Notes

Associated Lab(s) / Group(s): Tele-Supervised Autonomous Robotics

Associated Project(s): Wide Area Prospecting Using Supervised Autonomous Robots

Number of pages: 8

Text Reference

Yaron Rachlin, R. Negi, and Pradeep Khosla, "On the Interdependence of Sensing and Estimation Complexity in Sensor Networks," Proceedings of the Fifth International Symposium on Information Processing in Sensor Networks (IPSN '06), April, 2006.

BibTeX Reference

@inproceedings{Rachlin_2006_5419,
   author = "Yaron Rachlin and R. Negi and Pradeep Khosla",
   title = "On the Interdependence of Sensing and Estimation Complexity in Sensor Networks",
   booktitle = "Proceedings of the Fifth International Symposium on Information Processing in Sensor Networks (IPSN '06)",
   month = "April",
   year = "2006",
}

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