Authors: Nithya Gnanapandithan, Balasubramaniam Natarajan
Addresses: RELM Wireless Corporation, Lawrence, KS 66049, USA. ' Department of Electrical and Computer Engineering, Kansas State University, Kansas, USA
Abstract: In this paper, we use a novel Parallel Genetic Algorithm (PGA) approach to simultaneously optimise both the fusion rule and the local decision rules of a decentralised sensor network with correlated observations. The optimisation is performed with respect to the probability of error at the fusion centre. We show that our algorithm converges to a majority-like fusion rule irrespective of the degree of correlation and that the local decision rules play a key role in determining the performance of the overall system. By fixing the fusion rule and optimising only the local rules, we demonstrate that systems having different fusion rules can all provide similar performance if the local rules are chosen appropriately. We also show that the performance of the system degrades with increase in the correlation between the observations.
Keywords: distributed detection; sensor networks; correlated observations; sensor fusion; network performance; parallel genetic algorithms; decentralised networks; optimisation.
International Journal of Sensor Networks, 2007 Vol.2 No.3/4, pp.179 - 187
Available online: 11 Apr 2007 *Full-text access for editors Access for subscribers Purchase this article Comment on this article