Robust cooperative sensor network localization via the EM criterion in LOS/NLOS environments

We investigate robust cooperative localization in LOS/NLOS environments in wireless sensor networks. Round-trip time-of-arrival signal metric is considered so that time synchronization among sensors can be avoided. Owing to the non-line-of-sight effect, we model the measurement error by a two-mode Gaussian mixture distribution. However, its parameters are assumed completely unknown. We propose a centralized localization algorithm, which jointly estimates the unknown geographical coordinates and the nuisance mixture model parameters. The expectation-maximization criterion is adopted here to implement the maximum likelihood estimator. In addition, we also compute the Cramér-Rao lower bound (CRLB) for our estimation problem and present the best achievable positioning accuracy in terms of the CRLB

Research of Applying Machine Learning Methods to Outlier Detection in Wireless Sensor Networks

兵庫県立大学大学院201