opt.kz: Improve Accuracy of KZ Periodogram Estimation with...

In kzfs: Multi-Scale Motions Separation with Kolmogorov-Zurbenko Periodogram Signals

Description

Functions in this group are designed to improve the estimated wave parameters based on optimization of KZ directional periodograms and 2D periodograms.

Usage

optDR(a, rec, delta = 0.005, ...)

optD2P(a, rec, ...)

Arguments

a	Data array. Wave signals plus noise.
rec	Data list. For optDR, it is the outputs of function kzpdr. It includes the marked spectrum spike frequency values, directions.
delta	Searching range parameter for optimization. Default is 0.005.
...	Other arguments. k : Integer. The iterations number of KZFT. n : The sampling frequency rate as a multiplication of the Fourier frequencies

Details

optDR optimizes estimations of directional periodograms using R function stats::optimize. optD2P works for estimations of 2D periodograms; related optimization process is based on function stats::optim.

Value

optDR will return the data frame of detailed estimation for each direction.

See Also

kzpdr, kzp2

Examples

dx <- 300				# x range
dy <- 300				# y range
b <- expand.grid(x=1:dx, y=1:dy)
q1 <- pi/3; f1 <- 0.2;
b$v1 <- sin(f1*2*pi*(b$x*cos(q1)+b$y*sin(q1))+100*runif(1))
q2 <- pi/6; f2 <- 0.05;
b$v2 <- sin(f2*2*pi*(b$x*cos(q2)+b$y*sin(q2))+100*runif(1))
a <- array(0,c(dx,dy))
a[as.matrix(b[,1:2])] <- b$v1 + 1.5*b$v2
noise <- 5*matrix(rnorm(dx*dy,0,1),ncol=dy)

# Identifying with 2D periodogram
# kzp2.demo <- kzp2(a+noise)$kzp2d
QF <- kzp2.summary(kzp2.demo, num=2)

# Optimization of the 2D periodogram
# It may take 1 to 5 minutes
# kzp2.QF <- optD2P(a+noise, QF, k1=1, n1=1)
kzp2.QF

# Optimization of directional periodogram
# It may take 10 to 20 minutes
# kzpdr.demo <- kzpdr(a+noise, c(0,45,15,35)*pi/180, plot=TRUE, dpct=0.05)$rec
# kzpde.QF <- optDR(a+noise, kzpdr.demo)

QF2 <- kzpdr.eval(kzpdr.demo)
opt.QF2 <- kzpdr.eval(kzpdr.QF)

kzfs documentation built on June 2, 2019, 5:04 p.m.