Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Conditional dependence in distributed detection: how far can we go?
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Low-complexity algorithms for event detection in wireless sensor networks
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
Wireless Personal Communications: An International Journal
Hi-index | 754.84 |
In this paper, we consider the design of local decision rules for distributed detection systems where decisions from peripheral detectors are transmitted over dependent nonideal channels. Under the conditional independence assumption among multiple sensor observations, we show that the optimal detection performance can be achieved by employing likelihood-ratio quantizers (LRQ) as local decision rules under both the Bayesian criterion and Neyman-Pearson (NP) criterion even for the cases where the channels between the fusion center and local sensors are dependent and noisy. This work generalizes the previous work where independence among such channels was assumed. A person-by-person optimization (PBPO) procedure to obtain the solution is presented along with an illustrative example.