1,916 research outputs found
Bayesian kernel-based system identification with quantized output data
In this paper we introduce a novel method for linear system identification
with quantized output data. We model the impulse response as a zero-mean
Gaussian process whose covariance (kernel) is given by the recently proposed
stable spline kernel, which encodes information on regularity and exponential
stability. This serves as a starting point to cast our system identification
problem into a Bayesian framework. We employ Markov Chain Monte Carlo (MCMC)
methods to provide an estimate of the system. In particular, we show how to
design a Gibbs sampler which quickly converges to the target distribution.
Numerical simulations show a substantial improvement in the accuracy of the
estimates over state-of-the-art kernel-based methods when employed in
identification of systems with quantized data.Comment: Submitted to IFAC SysId 201
A new kernel-based approach to system identification with quantized output data
In this paper we introduce a novel method for linear system identification
with quantized output data. We model the impulse response as a zero-mean
Gaussian process whose covariance (kernel) is given by the recently proposed
stable spline kernel, which encodes information on regularity and exponential
stability. This serves as a starting point to cast our system identification
problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods
to provide an estimate of the system. In particular, we design two methods
based on the so-called Gibbs sampler that allow also to estimate the kernel
hyperparameters by marginal likelihood maximization via the
expectation-maximization method. Numerical simulations show the effectiveness
of the proposed scheme, as compared to the state-of-the-art kernel-based
methods when these are employed in system identification with quantized data.Comment: 10 pages, 4 figure
Identification of Parametric Underspread Linear Systems and Super-Resolution Radar
Identification of time-varying linear systems, which introduce both
time-shifts (delays) and frequency-shifts (Doppler-shifts), is a central task
in many engineering applications. This paper studies the problem of
identification of underspread linear systems (ULSs), whose responses lie within
a unit-area region in the delay Doppler space, by probing them with a known
input signal. It is shown that sufficiently-underspread parametric linear
systems, described by a finite set of delays and Doppler-shifts, are
identifiable from a single observation as long as the time bandwidth product of
the input signal is proportional to the square of the total number of delay
Doppler pairs in the system. In addition, an algorithm is developed that
enables identification of parametric ULSs from an input train of pulses in
polynomial time by exploiting recent results on sub-Nyquist sampling for time
delay estimation and classical results on recovery of frequencies from a sum of
complex exponentials. Finally, application of these results to super-resolution
target detection using radar is discussed. Specifically, it is shown that the
proposed procedure allows to distinguish between multiple targets with very
close proximity in the delay Doppler space, resulting in a resolution that
substantially exceeds that of standard matched-filtering based techniques
without introducing leakage effects inherent in recently proposed compressed
sensing-based radar methods.Comment: Revised version of a journal paper submitted to IEEE Trans. Signal
Processing: 30 pages, 17 figure
Short and long-term wind turbine power output prediction
In the wind energy industry, it is of great importance to develop models that
accurately forecast the power output of a wind turbine, as such predictions are
used for wind farm location assessment or power pricing and bidding,
monitoring, and preventive maintenance. As a first step, and following the
guidelines of the existing literature, we use the supervisory control and data
acquisition (SCADA) data to model the wind turbine power curve (WTPC). We
explore various parametric and non-parametric approaches for the modeling of
the WTPC, such as parametric logistic functions, and non-parametric piecewise
linear, polynomial, or cubic spline interpolation functions. We demonstrate
that all aforementioned classes of models are rich enough (with respect to
their relative complexity) to accurately model the WTPC, as their mean squared
error (MSE) is close to the MSE lower bound calculated from the historical
data. We further enhance the accuracy of our proposed model, by incorporating
additional environmental factors that affect the power output, such as the
ambient temperature, and the wind direction. However, all aforementioned
models, when it comes to forecasting, seem to have an intrinsic limitation, due
to their inability to capture the inherent auto-correlation of the data. To
avoid this conundrum, we show that adding a properly scaled ARMA modeling layer
increases short-term prediction performance, while keeping the long-term
prediction capability of the model
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