Outlier compensation in sensor network self-localization via the EM algorithm

Self-localization is an important component of distributed sensor systems. The presence of a few highly erroneous measurements, or outliers, results in erroneous sensor location estimates. In this paper, we employ the EM algorithm to iteratively detect outlier measurements and provide robust position estimates of the sensors. The derivation of the algorithm is given, and Monte-Carlo simulations are presented to compare this estimator to others. The performance of the EM-based algorithm is also shown to be close to the Cramer-Rao lower bound for position estimation when perfect knowledge of the outlier process is known.

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