scholarly journals Extracting the spectrum of a flow by spatial filtering

2018 ◽  
Vol 3 (12) ◽  
Author(s):  
Mahmoud Sadek ◽  
Hussein Aluie
Keyword(s):  
Spatial Filtering

Survey on Spatial Filtering Techniques

2012 ◽  
Vol 3 (4) ◽  
pp. 153-156
Author(s):  
A.Rajamani A.Rajamani ◽  
◽  
Dr.V.Krishnaveni Dr.V.Krishnaveni
Keyword(s):  
Spatial Filtering ◽  
Filtering Techniques

Spatial filtering of signals with spectrum overlapping

2019 ◽  
pp. 27-33
Author(s):  
M. E. Shevchenko ◽  
A. V. Gorovoy ◽  
S. N. Solovyov
Keyword(s):  
Antenna Arrays ◽  
A Priori ◽  
Spatial Filtering ◽  
Spatial Filter ◽  
Radio Sources ◽  
Probability Of Error ◽  
Shift Keying ◽  
A Priori Uncertainty

The paper considers the spatial filtering methods of signals with spectrum overlapping under conditions of a priori uncertainty of the directions of arrival from radio sources. The estimates of the directions of signals arrival obtained by ESPRIT or MUSIC are used in order to build a spatial filter. It is shown that when using ESPRIT, unlike MUSIC, an additional calculations of filter coefficients based on estimates of the directions of signals arrival are not required, and the quadrature components of the signals are formed simultaneously with estimates of the direction of their arrival. The probability of error performances of minimum shift keying signals which were divided by spatial filtering on the basis of ESPRIT and MUSIC using seven-element circular and angular antenna arrays are given.

scholarly journals A Multilevel Eigenvector Spatial Filtering Model of House Prices: A Case Study of House Sales in Fairfax County, Virginia

2019 ◽  
Vol 8 (11) ◽  
pp. 508
Author(s):  
Lan Hu ◽  
Yongwan Chun ◽  
Daniel A. Griffith
Keyword(s):  
Multilevel Model ◽  
House Prices ◽  
House Price ◽  
Spatial Filtering ◽  
Neighborhood Environment ◽  
Model Specification ◽  
Spatial Effects ◽  
Residential Areas ◽  
Fairfax County ◽  
Eigenvector Spatial Filtering

House prices tend to be spatially correlated due to similar physical features shared by neighboring houses and commonalities attributable to their neighborhood environment. A multilevel model is one of the methodologies that has been frequently adopted to address spatial effects in modeling house prices. Empirical studies show its capability in accounting for neighborhood specific spatial autocorrelation (SA) and analyzing potential factors related to house prices at both individual and neighborhood levels. However, a standard multilevel model specification only considers within-neighborhood SA, which refers to similar house prices within a given neighborhood, but neglects between-neighborhood SA, which refers to similar house prices for adjacent neighborhoods that can commonly exist in residential areas. This oversight may lead to unreliable inference results for covariates, and subsequently less accurate house price predictions. This study proposes to extend a multilevel model using Moran eigenvector spatial filtering (MESF) methodology. This proposed model can take into account simultaneously between-neighborhood SA with a set of Moran eigenvectors as well as potential within-neighborhood SA with a random effects term. An empirical analysis of 2016 and 2017 house prices in Fairfax County, Virginia, illustrates the capability of a multilevel MESF model specification in accounting for between-neighborhood SA present in data. A comparison of its model performance and house price prediction outcomes with conventional methodologies also indicates that the multilevel MESF model outperforms standard multilevel and hedonic models. With its simple and flexible feature, a multilevel MESF model can furnish an appealing and useful approach for understanding the underlying spatial distribution of house prices.

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