6,744 research outputs found
Robust Kalman tracking and smoothing with propagating and non-propagating outliers
A common situation in filtering where classical Kalman filtering does not
perform particularly well is tracking in the presence of propagating outliers.
This calls for robustness understood in a distributional sense, i.e.; we
enlarge the distribution assumptions made in the ideal model by suitable
neighborhoods. Based on optimality results for distributional-robust Kalman
filtering from Ruckdeschel[01,10], we propose new robust recursive filters and
smoothers designed for this purpose as well as specialized versions for
non-propagating outliers. We apply these procedures in the context of a GPS
problem arising in the car industry. To better understand these filters, we
study their behavior at stylized outlier patterns (for which they are not
designed) and compare them to other approaches for the tracking problem.
Finally, in a simulation study we discuss efficiency of our procedures in
comparison to competitors.Comment: 27 pages, 12 figures, 2 table
On credibility and robustness with the Kalman filter
Bühlmann (1967) gave a formal Bayesian derivation of the credibility ratio estimators that actuaries had been using for many years. Since then various generalizations of Bühlmann's model have appeared in the literature, each relaxing the i.i.d. assumptions in its own way. The introduction of weights is due to Bülhmann & Straub (1970) and that the regressors to Hachemeister (1975), but the first comprehensive actuarial application of the Kalman filter is due to de Jong & Zehnwirth (1983). More recent efforts have concentrated on the robustification of these estimators, as they provedı to be extremely sensitive to large claims. Kremer (1991) studies a robust regression credibility model and Künsch (1992) tackles the weighted case. Following Kremer (1994) we propose here a robust Kalman filter credibility model
Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids
Smart grid is a large complex network with a myriad of vulnerabilities,
usually operated in adversarial settings and regulated based on estimated
system states. In this study, we propose a novel highly secure distributed
dynamic state estimation mechanism for wide-area (multi-area) smart grids,
composed of geographically separated subregions, each supervised by a local
control center. We firstly propose a distributed state estimator assuming
regular system operation, that achieves near-optimal performance based on the
local Kalman filters and with the exchange of necessary information between
local centers. To enhance the security, we further propose to (i) protect the
network database and the network communication channels against attacks and
data manipulations via a blockchain (BC)-based system design, where the BC
operates on the peer-to-peer network of local centers, (ii) locally detect the
measurement anomalies in real-time to eliminate their effects on the state
estimation process, and (iii) detect misbehaving (hacked/faulty) local centers
in real-time via a distributed trust management scheme over the network. We
provide theoretical guarantees regarding the false alarm rates of the proposed
detection schemes, where the false alarms can be easily controlled. Numerical
studies illustrate that the proposed mechanism offers reliable state estimation
under regular system operation, timely and accurate detection of anomalies, and
good state recovery performance in case of anomalies
Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter
This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature
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