483 research outputs found
Tuning Windowed Chi-Squared Detectors for Sensor Attacks
A model-based windowed chi-squared procedure is proposed for identifying
falsified sensor measurements. We employ the widely-used static chi-squared and
the dynamic cumulative sum (CUSUM) fault/attack detection procedures as
benchmarks to compare the performance of the windowed chi-squared detector. In
particular, we characterize the state degradation that a class of attacks can
induce to the system while enforcing that the detectors do not raise alarms
(zero-alarm attacks). We quantify the advantage of using dynamic detectors
(windowed chi-squared and CUSUM detectors), which leverages the history of the
state, over a static detector (chi-squared) which uses a single measurement at
a time. Simulations using a chemical reactor are presented to illustrate the
performance of our tools
Underdetermined-order recursive least-squares adaptive filtering: The concept and algorithms
Published versio
On adaptive filter structure and performance
SIGLEAvailable from British Library Document Supply Centre- DSC:D75686/87 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
Automatic spectral density estimation for random fields on a lattice via bootstrap.
We consider the nonparametric estimation of spectral densities for secondorder stationary random fields on a d-dimensional lattice. We discuss some drawbacks of standard methods and propose modified estimator classes with improved bias convergence rate, emphasizing the use of kernel methods and the choice of an optimal smoothing number.We prove the uniform consistency and study the uniform asymptotic distribution when the optimal smoothing number is estimated from the sampled data.Spatial data; Spectral density; Smoothing number; Uniform asymptotic distribution; Bootstrap;
Automatic spectral density estimation for Random fields on a lattice via bootstrap
This paper considers the nonparametric estimation of spectral densities for second order stationary random fields on a d-dimensional lattice. I discuss some drawbacks of standard methods, and propose modified estimator classes with improved bias convergence rate, emphasizing the use of kernel methods and the choice of an optimal smoothing number. I prove uniform consistency and study the uniform asymptotic distribution, when the optimal smoothing number is estimated from the sampled data.
Subspace Tracking and Least Squares Approaches to Channel Estimation in Millimeter Wave Multiuser MIMO
The problem of MIMO channel estimation at millimeter wave frequencies, both
in a single-user and in a multi-user setting, is tackled in this paper. Using a
subspace approach, we develop a protocol enabling the estimation of the right
(resp. left) singular vectors at the transmitter (resp. receiver) side; then,
we adapt the projection approximation subspace tracking with deflation and the
orthogonal Oja algorithms to our framework and obtain two channel estimation
algorithms. We also present an alternative algorithm based on the least squares
approach. The hybrid analog/digital nature of the beamformer is also explicitly
taken into account at the algorithm design stage. In order to limit the system
complexity, a fixed analog beamformer is used at both sides of the
communication links. The obtained numerical results, showing the accuracy in
the estimation of the channel matrix dominant singular vectors, the system
achievable spectral efficiency, and the system bit-error-rate, prove that the
proposed algorithms are effective, and that they compare favorably, in terms of
the performance-complexity trade-off, with respect to several competing
alternatives.Comment: To appear on the IEEE Transactions on Communication
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