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The state estimation problem can be solved through different methods. In terms of robustness, an effective approach is represented by the Least Absolute Value (LAV) estimator, though vulnerable to leverage points. Based on a previously proposed theorem, in this paper we enunciate, and rigorously demonstrate, a new lemma that proves the identifiability of leverage points in LAV-based state estimation. On the basis of these theoretical foundations, we propose an algorithm for leverage point identification whose performance is validated by means of extensive numerical simulations and compared against more traditional approaches, like Projection Statistics (PS). The obtained results confirm that the proposed method outperforms PS and represents a significant enhancement for LAV-based state estimators as it correctly identifies all the leverage points, independently of the accuracy or the presence of measurement gross errors. A dedicated application example with respect to power system state estimation is finally included and discussed.
The development of advanced closed-loop irrigation systems requires accurate soil moisture information. In this work, we address the problem of soil moisture estimation for the agro-hydrological systems in a robust and reliable manner. A nonlinear st
In this work, we study estimation problems in nonlinear mechanical systems subject to non-stationary and unknown excitation, which are common and critical problems in design and health management of mechanical systems. A primary-auxiliary model sch
We consider the problem of communication allocation for remote state estimation in a cognitive radio sensor network~(CRSN). A sensor collects measurements of a physical plant, and transmits the data to a remote estimator as a secondary user (SU) in t
This work attempts to approximate a linear Gaussian system with a finite-state hidden Markov model (HMM), which is found useful in solving sophisticated event-based state estimation problems. An indirect modeling approach is developed, wherein a stat
Parameters of the mathematical model describing many practical dynamical systems are prone to vary due to aging or renewal, wear and tear, as well as changes in environmental or service conditions. These variabilities will adversely affect the accura