China
Africa
Explore chapters and articles related to this topic
Sampling and estimation theories
Published in John Bird, Bird's Engineering Mathematics, 2021
Estimation theory is a branch of statistics and signal processing that deals with estimating the values of parameters based on measured/empirical data that has a random component. Estimation theory can be found at the heart of many electronic signal processing systems designed to extract information; these systems include radar, sonar, speech, image, communications, control and seismology. This chapter introduces some of the principles involved with sampling and estimation theories.
Sampling and estimation theories
Published in John Bird, Engineering Mathematics, 2017
Estimation theory is a branch of statistics and signal processing that deals with estimating the values of parameters based on measured/empirical data that has a random component. Estimation theory can be found at the heart of many electronic signal processing systems designed to extract information; these systems include radar, sonar, speech, image, communications, control and seismology. This chapter introduces some of the principles involved with sampling and estimation theories.
Sampling and estimation theories
Published in John Bird, Bird's Higher Engineering Mathematics, 2021
Estimation theory is a branch of statistics and signal processing that deals with estimating the values of parameters based on measured/empirical data that has a random component. Estimation theory can be found at the heart of many electronic signal processing systems designed to extract information; these systems include radar, sonar, speech, image, communications, control and seismology. This chapter introduces some of the principles involved with sampling and estimation theories.
A dynamic detection method for polygonal wear of railway wheel based on parametric power spectral estimation
Published in Vehicle System Dynamics, 2022
Qiushi Wang, Zhongmin Xiao, Jinsong Zhou, Dao Gong, Zhanfei Zhang, Zegen Wang, Tengfei Wang, Yanling He
For the first time, a dynamic detection method for wheel polygonal wear based on parametric power spectral estimation is proposed to solve the above problem. Firstly, according to the dynamic characteristics of the wheel polygon, the harmonic frequency recovery model is established. Secondly, the order of the harmonic frequency recovery model is determined based on singular value decomposition and normalised error analysis. Then, the total least square method is used to calculate the parameters of the harmonic recovery model. Then, the power spectrum of the fault signal is evaluated according to the Cadzow estimation theory. Finally, the simulation signal and the measured axle-box vertical vibration acceleration signal of a metro vehicle are taken as case studies to verify the feasibility and effectiveness of the proposed method.
Rational (Padé) approximation for estimating the components of the partially-linear regression model
Published in Inverse Problems in Science and Engineering, 2021
Dursun Aydın, Ersin Yılmaz, Nur Chamidah
Let be an estimator of a k-dimensional parameters vector . In a regression problem, a cornerstone of parameter estimation theory is the bias and error incurred by an estimator with respect to a stated parameter . The common measure of error is the mean distribution error () matrix, which is given in Definition 4.1. To obtain matrix, variance, and bias of both methods and are shown as follows.
Statistical accuracy analysis for virtual reference feedback tuning with two degrees of freedom controllers*
Published in Systems Science & Control Engineering, 2020
Liu Qianming, Wang Jianhong, Wang Yanxiang
Accuracy analysis is one important factor in system identification and parameter estimation theory, as it can not only show the accuracy property for the parameter estimator, but also provide one useful tool to other research fields, such as optimal input design and model structure validation, etc. Two statistical variables are always used in accuracy analysis, i.e. expectation and variance, as variance can measure the approximation error between the true value and its estimator. If the approximation error is not tolerable, it means the parameter estimator is biased, and the whole process of system identification must be repeated until to obtain one unbiased estimator. The main contribution of this section is to derive the variance matrix for those two unknown parameter vectors through our own mathematical derivation.