Parameter Estimation in Wireless Sensor Networks with Normally Distributed Sensor Gains
Wireless Sensor Networks (WSN) has attracted significant attention recently. The distributed estimation problem is an important research topic in WSNs. In the distributed estimation problem, the fusion center estimates an unknown parameter based on information gathered from sensors. Usually, it is assumed that sensors have identical gains. However, this may not be true due to manufacture errors or environmental influence. In this paper, the authors assume sensor gains follow normal distribution and present Maximum Likelihood Estimation (MLE) approach for distributed estimation in WSNs with normally distributed sensor gains. Moreover, the Cramer-Rao Lower Bound (CRLB) corresponding to this MLE approach is also derived.