Time-varying spectral estimation using the instantaneous power spectrum (IPS)

Hippenstiel, Ralph D.; Oliveira, P.M.

doi:10.1109/29.60106

Cited by 30 publications

(11 citation statements)

References 13 publications

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“…Obtaining optimum window parameters can be di$cult if no information is available in advance about the likely fault conditions. The IPS is less sensitive to window parameter settings than many other time-frequency distributions [18]. Also, in many time-frequency methods, the analytical signal is required to perform the time-frequency representation.…”

Section: Instantaneous Power Spectrummentioning

confidence: 99%

“…For instance, an analytical signal and a higher than Nyquist sampling rate is essential when using the PWVD. However, the IPS can work with real-valued signals sampled at the traditional Nyquist rate [18].…”

Section: Instantaneous Power Spectrummentioning

confidence: 99%

“…In most time-frequency distributions, the interference terms are located equidistantly between the main components. With the IPS, however, the interference is not a dominant feature of the distribution, because the terms coincide with the main components rather than appearing between them [14,18], and therefore it is less likely that pertinent features will be masked by them. As a consequence, the interpretation of an IPS representation will likely be easier than that of say, a PWVD equivalent.…”

Section: Instantaneous Power Spectrummentioning

confidence: 99%

See 2 more Smart Citations

Detection of Gear Deterioration Under Varying Load Conditions by Using the Instantaneous Power Spectrum

Baydar¹,

Ball²

2000

Mechanical Systems and Signal Processing

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Section: Instantaneous Power Spectrummentioning

confidence: 99%

Section: Instantaneous Power Spectrummentioning

confidence: 99%

Section: Instantaneous Power Spectrummentioning

confidence: 99%

See 1 more Smart Citation

Detection of Gear Deterioration Under Varying Load Conditions by Using the Instantaneous Power Spectrum

Baydar¹,

Ball²

2000

Mechanical Systems and Signal Processing

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“…Amongst the literature there are many parameters under investigation for the purposes of modulation recognition [1,2,4,5,13,14,15,17,18,19,20]. They include characteristics such as power-spectral density (PSD), signal-noise ratio (SNR), bandwidth, instantaneous frequency and amplitude, and statistical measures (including high-order moments and cumulants).…”

Section: Parameters For Modulation Recognitionmentioning

confidence: 99%

Parameters for automatic modulation recognition of HF signals

Giesbrecht¹,

Clarke²,

Abbott³

2006

10th IET International Conference on Ionospheric Radio Systems and Techniques (IRST 2006)

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Two fundamental processes of modulation recognition are parameter extraction and feature selection. Parameter extraction determines unique characteristics of the signal such as frequency, phase, and amplitude. Parameters may also include statistical measures such as standard deviation, and nth-order moments and cumulants. This paper provides a brief review of two parameters, coherence and entropic distance, and reintroduces a statistical measure of signal-to-noise ratio (SNR). It also discusses the usefulness of these parameters in automatic modulation recognition ofHF signals.

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“…2) Type I and Type II Page Spectrum: Page defined the instantaneous power spectrum [1], [2], [13], [17] (18) with . This spectrum can be rewritten as It belongs to the class of type I spectra and will here be referred to as type I Page spectrum.…”

Section: ) Generalized Wigner-ville Spectrum and Generalized Evolutimentioning

confidence: 99%

Nonstationary spectral analysis based on time-frequency operator symbols and underspread approximations

Matz

Hlawatsch

2006

IEEE Trans. Inform. Theory

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Abstract-We present a unified framework for time-varying or time-frequency (TF) spectra of nonstationary random processes in terms of TF operator symbols. We provide axiomatic definitions and TF operator symbol formulations for two broad classes of TF spectra, one of which is new. These classes contain all major existing TF spectra such as the Wigner-Ville, evolutionary, instantaneous power, and physical spectrum. Our subsequent analysis focuses on the practically important case of nonstationary processes with negligible high-lag TF correlations (so-called underspread processes). We demonstrate that for underspread processes all TF spectra yield effectively identical results and satisfy several desirable properties at least approximately. We also show that Gabor frames provide approximate Karhunen-Loève (KL) functions of underspread processes and TF spectra provide a corresponding approximate KL spectrum. Finally, we formulate simple approximate input-output relations for the TF spectra of underspread processes that are passed through underspread linear timevarying systems. All approximations are substantiated mathematically by upper bounds on the associated approximation errors. Our results establish a TF calculus for the second-order analysis and time-varying filtering of underspread processes that is as simple as the conventional spectral calculus for stationary processes.Index Terms-Evolutionary spectrum, Gabor expansion, instantaneous power spectrum, Karhunen-Loève (KL) expansion, nonstationary random processes, nonstationary spectral analysis, time-frequency (TF) analysis, time-varying systems, Wigner-Ville spectrum.

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Time-varying spectral estimation using the instantaneous power spectrum (IPS)

Cited by 30 publications

References 13 publications

Detection of Gear Deterioration Under Varying Load Conditions by Using the Instantaneous Power Spectrum

Detection of Gear Deterioration Under Varying Load Conditions by Using the Instantaneous Power Spectrum

Parameters for automatic modulation recognition of HF signals

Nonstationary spectral analysis based on time-frequency operator symbols and underspread approximations

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