TY - JFULL
AU - Amir Rastegarnia and Mohammad Ali Tinati and Azam Khalili
PY - 2009/12/
TI - Distributed Estimation Using an Improved Incremental Distributed LMS Algorithm
T2 - International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering
SP - 2020
EP - 2025
EM - a rastegar@ieee.org, a-khalili@tabrizu.a.cir, tinati@tabrizu.ac.ir
VL - 3
SN - 1307-6892
UR - http://waset.org/publications/8050
PU - World Academy of Science, Engineering and Technology
NX - International Science Index 35, 2009
N2 - In this paper we consider the problem of distributed adaptive estimation in wireless sensor networks for two different observation noise conditions. In the first case, we assume that there are some sensors with high observation noise variance (noisy sensors) in the network. In the second case, different variance for observation noise is assumed among the sensors which is more close to real scenario. In both cases, an initial estimate of each sensor-s observation noise is obtained. For the first case, we show that when there are such sensors in the network, the performance of conventional distributed adaptive estimation algorithms such as incremental distributed least mean square (IDLMS) algorithm drastically decreases. In addition, detecting and ignoring these sensors leads to a better performance in a sense of estimation. In the next step, we propose a simple algorithm to detect theses noisy sensors and modify the IDLMS algorithm to deal with noisy sensors. For the second case, we propose a new algorithm in which the step-size parameter is adjusted for each sensor according to its observation noise variance. As the simulation results show, the proposed methods outperforms the IDLMS algorithm in the same condition.
ER -