Abstract
Industrial wireless sensor networks (WSNs) often operate under harsh conditions that require robustness from an estimator of a measured quantity. We propose a novel distributed unbiased finite-impulse response (UFIR) filter called micro-UFIR filter that, unlike the micro-Kalman filter (micro-KF), is robust against modeling errors in uncertain noise environments. The micro-UFIR filter is derived based on average consensus on measurements and, unlike the micro-KF, requires only one consensus filter. Better robustness of the micro-UFIR filter is shown analytically and confirmed by simulations of a WSN and a vehicle travelling along a circular trajectory under unpredictable impacts, impulsive noise, and errors in the noise statistics.
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