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We study sparse gross error correction for state estimation in a non-linear sensing system. We consider a practical assumption that gross errors are sparse, and their locations tend to be invariant over a few consecutive measurement periods. Under the assumption, a robust state estimation and error correction algorithm using multiple measurement vectors is proposed based on local linear approximation...
In this paper, we present feasibility study of sparse error correction in power system measurements. Legacy bad data detection mechanisms have been shown to be prone to make erroneous decisions when elaborately designed errors are injected by cyber attacks. In order to effectively handle such gross errors, sparse error correction framework has been suggested in the literature. For proper utilization...
In this paper, we examine an approach for robust state estimation that exploits the sparse nature of gross errors in sensor system measurements and study the feasibility of gross error identification. Under the practical assumption that potential locations of gross errors remain fixed during multiple measurement periods, gross error correction based on multiple measurement vectors is proposed. Our...
In applications including radar and ultrasonic inspection, an observed signal can often be modeled as the output over a finite interval of a linear, time-invariant (LTI) operator having a short-duration impulse response. For processing such as filtering and compression it would be useful to have approximations to eigenfunctions of the operator. It has been shown that discrete-time complex exponentials...
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