10,483 research outputs found
Interpolated-DFT-Based Fast and Accurate Amplitude and Phase Estimation for the Control of Power
The quality of energy produced in renewable energy systems has to be at the
high level specified by respective standards and directives. The estimation
accuracy of grid signal parameters is one of the most important factors
affecting this quality. This paper presents a method for a very fast and
accurate amplitude and phase grid signal estimation using the Fast Fourier
Transform procedure and maximum decay sidelobes windows. The most important
features of the method are the elimination of the impact associated with the
conjugate's component on the results and the straightforward implementation.
Moreover, the measurement time is very short - even far less than one period of
the grid signal. The influence of harmonics on the results is reduced by using
a bandpass prefilter. Even using a 40 dB FIR prefilter for the grid signal with
THD = 38%, SNR = 53 dB and a 20-30% slow decay exponential drift the maximum
error of the amplitude estimation is approximately 1% and approximately 0.085
rad of the phase estimation in a real-time DSP system for 512 samples. The
errors are smaller by several orders of magnitude for more accurate prefilters.Comment: in Metrology and Measurement Systems, 201
Spectral Compressive Sensing with Model Selection
The performance of existing approaches to the recovery of frequency-sparse
signals from compressed measurements is limited by the coherence of required
sparsity dictionaries and the discretization of frequency parameter space. In
this paper, we adopt a parametric joint recovery-estimation method based on
model selection in spectral compressive sensing. Numerical experiments show
that our approach outperforms most state-of-the-art spectral CS recovery
approaches in fidelity, tolerance to noise and computation efficiency.Comment: 5 pages, 2 figures, 1 table, published in ICASSP 201
Characterization of the Crab Pulsar's Timing Noise
We present a power spectral analysis of the Crab pulsar's timing noise,
mainly using radio measurements from Jodrell Bank taken over the period
1982-1989. The power spectral analysis is complicated by nonuniform data
sampling and the presence of a steep red power spectrum that can distort power
spectra measurement by causing severe power ``leakage''. We develop a simple
windowing method for computing red noise power spectra of uniformly sampled
data sets and test it on Monte Carlo generated sample realizations of red
power-law noise. We generalize time-domain methods of generating power-law red
noise with even integer spectral indices to the case of noninteger spectral
indices. The Jodrell Bank pulse phase residuals are dense and smooth enough
that an interpolation onto a uniform time series is possible. A windowed power
spectrum is computed revealing a periodic or nearly periodic component with a
period of about 568 days and a 1/f^3 power-law noise component with a noise
strength of 1.24 +/- 0.067 10^{-16} cycles^2/sec^2 over the analysis frequency
range 0.003 - 0.1 cycles/day. This result deviates from past analyses which
characterized the pulse phase timing residuals as either 1/f^4 power-law noise
or a quasiperiodic process. The analysis was checked using the Deeter
polynomial method of power spectrum estimation that was developed for the case
of nonuniform sampling, but has lower spectral resolution. The timing noise is
consistent with a torque noise spectrum rising with analysis frequency as f
implying blue torque noise, a result not predicted by current models of pulsar
timing noise. If the periodic or nearly periodic component is due to a binary
companion, we find a companion mass > 3.2 Earth masses.Comment: 53 pages, 9 figures, submitted to MNRAS, abstract condense
OMP-type Algorithm with Structured Sparsity Patterns for Multipath Radar Signals
A transmitted, unknown radar signal is observed at the receiver through more
than one path in additive noise. The aim is to recover the waveform of the
intercepted signal and to simultaneously estimate the direction of arrival
(DOA). We propose an approach exploiting the parsimonious time-frequency
representation of the signal by applying a new OMP-type algorithm for
structured sparsity patterns. An important issue is the scalability of the
proposed algorithm since high-dimensional models shall be used for radar
signals. Monte-Carlo simulations for modulated signals illustrate the good
performance of the method even for low signal-to-noise ratios and a gain of 20
dB for the DOA estimation compared to some elementary method
Frequency Tracking and Parameter Estimation for Robust Quantum State-Estimation
In this paper we consider the problem of tracking the state of a quantum
system via a continuous measurement. If the system Hamiltonian is known
precisely, this merely requires integrating the appropriate stochastic master
equation. However, even a small error in the assumed Hamiltonian can render
this approach useless. The natural answer to this problem is to include the
parameters of the Hamiltonian as part of the estimation problem, and the full
Bayesian solution to this task provides a state-estimate that is robust against
uncertainties. However, this approach requires considerable computational
overhead. Here we consider a single qubit in which the Hamiltonian contains a
single unknown parameter. We show that classical frequency estimation
techniques greatly reduce the computational overhead associated with Bayesian
estimation and provide accurate estimates for the qubit frequencyComment: 6 figures, 13 page
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