Asymptotic Properties of Least Squares Estimators and Sequential Least Squares Estimators of a Chirp-like Signal Model Parameters

Grover, Rhythm; Kundu, Debasis; Mitra, Amit

doi:10.1007/s00034-021-01724-7

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…For discrete time t and random noise, being a linear time series, some results on consistency and asymptotic normality of LSE and some other estimates of a chirp-signal which is one harmonic or the sum of harmonics (1), have been obtained in a large numbers of works. We will point only to publications Nandi and Kundu (2004), Kundu and Nandi (2008), Lahiri (2011), Lahiri, Kundu, andMitra (2015), Nandi and Kundu (2020), Kundu and Nandi (2021), Grover, Kundu, and Mitra (2021) and references there in.…”

Section: Setting and Main Resultsmentioning

confidence: 99%

Consistency of the LSE for Chirp Signal Parameters in the Models with Strongly and Weakly Dependent Noise

Ivanov,

Hladun

2023

AJS

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Section: Setting and Main Resultsmentioning

confidence: 99%

Consistency of the LSE for Chirp Signal Parameters in the Models with Strongly and Weakly Dependent Noise

Ivanov,

Hladun

2023

AJS

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“…( 2 ), the memory block has backlash nonlinearity. The backlash characteristic widely exists in various pieces of mechanical equipment due to the presence of gears 34 , 35 . Hence, we use the backlash submodel to represent memory nonlinearity.…”

Section: Problem Statementmentioning

confidence: 99%

Adaptive parameter estimation for the expanded sandwich model

Yang

Zhang

Liu

et al. 2023

Sci Rep

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An expanded-sandwich system is a nonlinear extended block-oriented system in which memoryless elements in conventional block-oriented systems are displaced by memory submodels. Expanded-sandwich system identification has received extensive attention in recent years due to the powerful ability of these systems to describe actual industrial systems. This study proposes a novel recursive identification algorithm for an expanded-sandwich system, in which an estimator is developed on the basis of parameter identification error data rather than the traditional prediction error output information. In this scheme, a filter is introduced to extract the available system information based on miserly structure layout, and some intermediate variables are designed using filtered vectors. According to the developed intermediate variables, the parameter identification error data can be obtained. Thereafter, an adaptive estimator is established by integrating the identification error data compared with the classic adaptive estimator based on the prediction error output information. Thus, the design framework introduced in this research provides a new perspective for the design of identification algorithms. Under a general continuous excitation condition, the parameter estimation values can converge to the true values. Finally, experimental results and illustrative examples indicate the availability and usefulness of the proposed method.

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“…For discrete time t and random noise, that is, for a linear time series, some results on consistency and asymptotic normality of LSE of chirp signal parameters have been obtained in many works. We will only point to publications [23,18,20,21,24,19,7] and references there in.…”

Section: Introductionmentioning

confidence: 99%