View source for On Decentralized Classification using a Network of Mobile Sensors

{{HTDB paper
| authors = 
Timothy H. Chung, Joel W. Burdick, Richard M. Murray
| title = On Decentralized Classification using a Network of Mobile Sensors
| source = Submitted, 2005 International Conference on Robotics and Automation
| year = 2004
| type = 
Conference Paper
| funding = NSF/ITR
| url = http://www.cds.caltech.edu/~murray/preprints/cbm05-icra.pdf
| abstract = 
This paper considers how a team of mobile sensors
should cooperatively move so as to optimally categorize a
single moving target from their noisy sensor readings. The
cooperative control procedure is based on the development of
a cost function that quantifies the teamâs classification error.
The robotsâ motions are then chosen to minimize this function.
We particularly investigate the case where the sensor noise and
class distributions are Gaussian. In this case, we can derive a
duality principle which states that optimal classification will be
realized when the covariance of the target estimate is minimized.
That is, in this case, optimal estimation leads naturally to
optimal classification. We extend previous work to develop a
distributed discrete-gradient search algorithm that guides the
teamâs location motions for purposes of optimal estimation and
classification. The concepts developed are validated through
numerical studies.
| flags = 
| tag = cbm05-icra
| id = 2004t
}}