| 1. |
- Zhang, Kewei, et al.
(författare)
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Protecting GNSS Open Service-Navigation Message Authentication against Distance-Decreasing Attacks
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- As the security of global navigation satellite systems (GNSS) for civilian usage is increasingly important, navigation message authentication (NMA) significantly improves resilience to spoofing attacks. However, not all attacks can be effectively countered: a strong variant of replay/relay attacks, distance-decreasing (DD) attacks, can shorten pseudorange measurements, without manipulating the cryptographically protected navigation message, thus manipulating the position, velocity, and time solution undetected. First, we discuss how DD attacks can tamper with GNSS signals, demonstrating the attack effectiveness on a recorded Galileo signal. DD attacks might introduce bit errors to the forged signals, but the adversary can keep this error rate very low with proper attack parameter settings. Then, based on our mathematical model of the prompt correlator output of the tracking phase at the victim receiver, we find that the correlator output distribution changes in the presence of DD attacks. This leads us to apply hypothesis testing to detect DD attacks, notably a Goodness of Fit (GoF) test and a generalized likelihood ratio test (GLRT), depending on the victim’s knowledge on the DD attacks. Monte Carlo simulations are used to evaluate the detection probability and the receiver operating characteristic (ROC) curves for two tests, for different adversary configuration and noise settings. Then, we evaluate the effectiveness of the GoF and GLRT tests with a synthesized DD signal. Both tests can detect DD attacks with similar performance in high signal-to-noise ratio (SNR) environments. The GLRT detection probability is approximately 20% higher than that of the GoF test in low SNR environments.
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| 2. |
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| 3. |
- del Aguila Pla, Pol, 1990-, et al.
(författare)
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Clock synchronization over networks - Identifiability of the sawtooth model
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we analyze the two-node joint clocksynchronization and ranging problem. We focus on the case of nodes that employ time-to-digital converters to determine the range between them precisely. This specific design leads to a sawtooth model for the captured signal, which has not been studied in detail before from an estimation theory standpoint. In the study of this model, we recover the basic conclusion of a well-known article by Freris, Graham, and Kumar in clock synchronization. Additionally, we discover a surprising identifiability result on the sawtooth signal model: noise improves the theoretical condition of the estimation of the phase and offset parameters. To complete our study, we provide performance references for joint clock synchronization and ranging. In particular, we present the Cramér-Rao lower bounds that correspond to a linearization of our model, as well as a simulation study on the practical performance of basic estimation strategies under realistic parameters. With these performance references, we enable further research in estimation strategies using the sawtooth model and pave the path towards industrial use.
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| 4. |
- Yin, Hang, et al.
(författare)
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Policy Learning with Embedded Koopman Optimal Control
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Embedding an optimization process has been explored for imposing efficient and flexible policy structures. Existing work often build upon nonlinear optimization with explicitly unrolling of iteration steps, making policy inference prohibitively expensive for online learning and real-time control. Our approach embeds a linear-quadratic-regulator (LQR) formulation with a Koopman representation, thus exhibiting the tractability from a closed-form solution and richness from a non-convex neural network. We use a few auxiliary objectives and reparameterization to enforce optimality conditions of the policy that can be easily integrated to standard gradient-based learning. Our approach is shown to be effective for learning policies rendering an optimality structure and efficient reinforcement learning, including simulated pendulum control, 2D and 3D walking, and manipulation for both rigid and deformable objects. We also demonstrate real world application in a robot pivoting task.
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| 5. |
- Anubhab, Ghosh, et al.
(författare)
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DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods employ maximum likelihood or Bayesian estimation. However, these methods suffer from some limitations, most notably the substantial computational time for inference coupled with limited flexibility in application. In this work, we propose DeepBayes estimators that leverage the power of deep recurrent neural networks in learning an estimator. The method consists of first training a recurrent neural network to minimize the mean-squared estimation error over a set of synthetically generated data using models drawn from the model set of interest. The a priori trained estimator can then be used directly for inference by evaluating the network with the estimation data. The deep recurrent neural network architectures can be trained offline and ensure significant time savings during inference. We experiment with two popular recurrent neural networks -- long short term memory network (LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our proposed method on different example models and perform detailed comparisons with state-of-the-art approaches. We also provide a study on a real-world nonlinear benchmark problem. The experimental evaluations show that the proposed approach is asymptotically as good as the Bayes estimator.
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| 6. |
- Birk, Wolfgang, et al.
(författare)
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Project: iRoad
- 2011
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Annan publikation (populärvet., debatt m.m.)
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| 7. |
- Carlson, Johan, et al.
(författare)
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Projekt: SCOPE Norra
- 2011
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Annan publikation (populärvet., debatt m.m.)abstract
- SCOPE Norra är ett samarbetskonsortium för forskning och utveckling tillsammans med massa- och pappersindustrin i Norrbotten och Västerbotten. Projektet koordineras av centrumbildningen ProcessIT Innovations.Inom SCOPE Norra pågår ett flertal delprojekt, uppdelat på ett antal fokusområden.Huvudfinansiär för konsortiet är Tillväxtverket genom medel från EU:s strukturfonder.
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| 8. |
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| 9. |
- Wik, Torsten, 1968, et al.
(författare)
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Power and current limit estimation for batteries
- 2014
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The present invention relates to a method and a system for determining a charge current and a discharge current of an energy storage cell.
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