Separability of scalar random multisine signals

Enqvist, Martin

doi:10.1016/j.automatica.2011.05.015

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Furthermore, (McGraw & Wagner, 1968) have shown that the signals belonging to the class of elliptically symmetric distributions are separable. Also random phase multisines with flat amplitude spectra are proven to be separable in (Enqvist, 2011). A more indepth discussion of separable processes for nonlinear system identification can be found in (Billings & Fakhouri, 1978b;Enqvist & Ljung, 2005;Enqvist, 2010).…”

Section: Separable Processesmentioning

confidence: 99%

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

2017

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Block-oriented nonlinear models are popular in nonlinear system identification because of their advantages of being simple to understand and easy to use. Many different identification approaches were developed over the years to estimate the parameters of a wide range of block-oriented nonlinear models. One class of these approaches uses linear approximations to initialize the identification algorithm. The best linear approximation framework and the -approximation framework, or equivalent frameworks, allow the user to extract important information about the system, guide the user in selecting good candidate model structures and orders, and prove to be a good starting point for nonlinear system identification algorithms. This paper gives an overview of the different block-oriented nonlinear models that can be identified using linear approximations, and of the identification algorithms that have been developed in the past. A non-exhaustive overview of the most important other block-oriented nonlinear system identification approaches is also provided throughout this paper.

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Section: Separable Processesmentioning

confidence: 99%

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

2017

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“…The formula ( 26) can be exploited in practice to develop a new approach to real-time spectral analysis of separable processes [24]. The class of separable random processes include [21]- [23]: 1. A Gaussian process, and also other elliptically symmetric processes; 2.…”

Section: B Crosslation Function Of a Separable Processmentioning

confidence: 99%

Zero-Crossing Waveform Interferometry: an Alternative to Correlation in Signal Processing

Szajnowski

2018

Preprint

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It is shown that multiple representations (such as replicas or Hilbert transforms) of a random waveform can interfere constructively to form a compact pattern, akin to a wave packet, when the representations are created in synchrony with zero crossings of the waveform. A function of such 'engineered' zero-crossing interferograms can exhibit time-delay resolution superior to that associated with a conventional correlation function, especially for waveforms with slowly-decaying power spectra. A phenomenon of local slew rate at zero crossings is exploited to substantially reduce the Cramér-Rao bound on timedelay estimators. A system, based on a concept of elapsed time, is proposed to determine zero-crossing interferograms in real time.

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