scholarly journals Applications of the Periodogram Method for Perturbed Block Toeplitz Matrices in Statistical Signal Processing

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 582
Author(s):  
Jesús Gutiérrez-Gutiérrez ◽  
Xabier Insausti ◽  
Marta Zárraga-Rodríguez
Keyword(s):  
Signal Processing ◽  
Parameter Estimation ◽  
Moving Average ◽  
Toeplitz Matrices ◽  
Estimation Method ◽  
Cholesky Decomposition ◽  
Statistical Signal Processing ◽  
Correlation Matrices ◽  
Vector Autoregressive ◽  
Block Toeplitz Matrices

In this paper, we combine the periodogram method for perturbed block Toeplitz matrices with the Cholesky decomposition to give a parameter estimation method for any perturbed vector autoregressive (VAR) or vector moving average (VMA) process, when we only know a perturbed version of the sequence of correlation matrices of the process. In order to combine the periodogram method for perturbed block Toeplitz matrices with the Cholesky decomposition, we first need to generalize a known result on the Cholesky decomposition of Toeplitz matrices to perturbed block Toeplitz matrices.

scholarly journals Auto-regressive moving average parameter estimation for 1/f process under colored Gaussian noise background

2019 ◽  
Vol 13 ◽  
pp. 174830261986743
Author(s):  
Chen Wang ◽  
Yao-Wu Shi ◽  
Lan-Xiang Zhu ◽  
Li-Fei Deng ◽  
Yi-Ran Shi ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Data Acquisition ◽  
Moving Average ◽  
Estimation Method ◽  
Low Noise ◽  
Low Noise Amplifiers ◽  
Dual Channel ◽  
Data Acquisition Card ◽  
Auto Regressive ◽  
Cross Correlations

Current algorithms for estimating auto-regressive moving average parameters of transistor 1/f process are usually under noiseless background. Transistor noises are measured by a non-destructive cross-spectrum measurement technique, with transistor noise first passing through dual-channel ultra-low noise amplifiers, then inputting the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output transistor 1/f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear SαS processes, and propose a samples normalized cross-correlations-based auto-regressive moving average parameter estimation method effective in noisy environments. Simulation results of auto-regressive moving average parameter estimation exhibit good performance.

scholarly journals Functions of banded Hermitian block Toeplitz matrices in signal processing

2007 ◽  
Vol 422 (2-3) ◽  
pp. 788-807 ◽  
Author(s):  
Jesús Gutiérrez-Gutiérrez ◽  
Pedro M. Crespo ◽  
Albrecht Böttcher
Keyword(s):  
Signal Processing ◽  
Toeplitz Matrices ◽  
Block Toeplitz Matrices

A Two Dimension Signal Parameter Estimation Method Based on Super Resolution Technology

2013 ◽  
Vol 712-715 ◽  
pp. 2007-2014
Author(s):  
Ying Xu ◽  
Jun Zhao
Keyword(s):  
Signal Processing ◽  
Parameter Estimation ◽  
Array Signal Processing ◽  
Estimation Method ◽  
Super Resolution ◽  
Parameters Estimation ◽  
Joint Estimation ◽  
Spectrum Estimation ◽  
Processing Parameter ◽  
Music Algorithm

The 2D spectrum estimation based on uniform linear array is studied in this paper. MUSIC method is firstly introduced into array signal processing to realize the frequency and angle joint estimation. And the modified MUSIC method is then applied for two dim array signal processing to fulfill joint parameters estimation of coherent signal sources. Simulation results show the validity of the proposed method. Keywords: Super resolution;Array signal processing;Parameter estimation;MUSIC algorithm

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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