Reconstruction of Stationary Processes Sampled at Random Times

Lacaze, B.

doi:10.1007/978-1-4615-1229-5_8

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…Holding (10) in (9) , a summation leads to (we want eliminate the Z k (nT + θ) which are not observed and we want replace them by the Z(nT + θ) which are the data, using (8))…”

Section: Periodic Nonstationary Sampling Plan (Pns)mentioning

confidence: 99%

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Reconstruction from Periodic Nonstationary Sampling (PNS) without Resampling

Lacaze¹

2017

STSIP

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Periodic Nonuniform Samplings (PNS) are concatenations of periodic samplings of same periods but which are shifted. They allow to suppress aliasings, dead times of samplers by devices which provide a resampling of data. The obtained periodic sequence is treated through usual methods. In this paper, we show that the resampling stage is unnecessary, that PNS allow good estimations without resampling, at the same time for the sampled process, for linear filterings and for power spectra.

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“…Holding (10) in (9) , a summation leads to (we want eliminate the Z k (nT + θ) which are not observed and we want replace them by the Z(nT + θ) which are the data, using (8))…”

Section: Periodic Nonstationary Sampling Plan (Pns)mentioning

confidence: 99%

“…In the case of a PNSN of basic period T, we have μ = T /N, μ = 0. The "Lagrange estimator" Z (t) could be defined from limits of Lagrange polynomials, elsewhere extensively covered in the literature of analytic functions [8], [9]…”

Section: Example 2: Two-bands Processesmentioning

confidence: 99%

Reconstruction from Periodic Nonstationary Sampling (PNS) without Resampling

Lacaze¹

2017

STSIP

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show abstract

“…A noniternative approach is given, which assumes periodic time grids, that is, the non-uniformity is repeated. Another overview of techniques for non-uniform sampling is given in [10], where, for example, Ferreira [11] studies the special case of recovery of missing data and Lacaze [12] reconstructs stationary processes.…”

Section: Uniform Sampling In Secondary Domainmentioning

confidence: 99%

Downsampling Non-Uniformly Sampled Data

Eng

Gustafsson

2007

EURASIP J. Adv. Signal Process.

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Decimating a uniformly sampled signal a factor D involves low-pass antialias filtering with normalized cutoff frequency 1/D followed by picking out every Dth sample. Alternatively, decimation can be done in the frequency domain using the fast Fourier transform (FFT) algorithm, after zero-padding the signal and truncating the FFT. We outline three approaches to decimate nonuniformly sampled signals, which are all based on interpolation. The interpolation is done in different domains, and the intersample behavior does not need to be known. The first one interpolates the signal to a uniformly sampling, after which standard decimation can be applied. The second one interpolates a continuous-time convolution integral, that implements the antialias filter, after which every Dth sample can be picked out. The third frequency domain approach computes an approximate Fourier transform, after which truncation and IFFT give the desired result. Simulations indicate that the second approach is particularly useful. A thorough analysis is therefore performed for this case, using the assumption that the non-uniformly distributed sampling instants are generated by a stochastic process.

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Reconstruction formula for irregular sampling

Lacaze

2005

STSIP

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Reconstruction of Stationary Processes Sampled at Random Times

Cited by 10 publications

References 22 publications

Reconstruction from Periodic Nonstationary Sampling (PNS) without Resampling

Reconstruction from Periodic Nonstationary Sampling (PNS) without Resampling

Downsampling Non-Uniformly Sampled Data

Reconstruction formula for irregular sampling

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