simulating stationary Gaussian field over an 'm' times 'n' grid
- 'm' and 'n' for evaluating the field over the m*n grid;
note that size of covariance matrix is m^2*n^2;
- scalar function rho(h), where 'h' is a two dimensional vector
input and cov(X_t,Y_s)=rho(t-s) is the cov. function of a
2-dimensional stationary Gaussian field; see reference below;
- two statistically independent fields 'field1' and 'field2'
over the m*n grid;
- vectors 'tx' and 'ty' so that the field is plotted via
*exp(-(h(1)^2/50^2+h(2)^2/15^2))); % define covariance function
stationary_Gaussian_process(512,384,rho); % plot when no output wanted
Kroese, D. P., & Botev, Z. I. (2015). Spatial Process Simulation.
In Stochastic Geometry, Spatial Statistics and Random Fields(pp. 369-404)
Springer International Publishing, DOI: 10.1007/978-3-319-10064-7_12
Please help me to understand what does it mean and tell me how to do it-
Realistic spatially correlated speckle noise in ultrasound images can be simulated by low-pass filtering a complex Gaussian random field and taking the magnitude of filtered output.The speckle noise with different covariance functions are introduced under different noise cases.These are-Matern covariance function,Spherical covariance function,Exponential covariance function and Rational quadratic covariance function.
Mean of Gaussian random field=0
Very helpful code.
I am interested in How to use this circulant embedding method to generate a 3 dimensional stationary processes. The book does not describe that case in detail. Could we have a further communication?
Thank you for your consideration.
- rewritten as an m-file