Quantcast

Documentation Center

  • Trial Software
  • Product Updates

spectrum.mtm

Thomson multitaper spectrum

Syntax

Hs = spectrum.mtm
Hs = spectrum.mtm(TimeBW)
Hs = spectrum.mtm(DPSS,Concentrations)
Hs = spectrum.mtm(...,CombineMethod)

Description

    Note:   The use of spectrum.mtm is not recommended. Use pmtm instead.

Hs = spectrum.mtm returns a default Thomson multitaper spectrum object, Hs that defines the parameters for the Thomson multitaper spectral estimation algorithm, which uses a linear or nonlinear combination of modified periodograms. The periodograms are computed using a sequence of orthogonal tapers (windows in the frequency domain) specified from discrete prolate spheroidal sequences (dpss). This object uses the following default values:

Property NameDefault ValueDescription

TimeBW

4

Product of time and bandwidth for the discrete prolate spheroidal sequences (or Slepian sequences) used as data windows

CombineMethod

'adaptive'

Algorithm for combining the individual spectral estimates. Valid values are 'adaptive' — adaptive (nonlinear) 'unity' — unity weights (linear) 'eigenvector' — Eigenvalue weights (linear)

Hs = spectrum.mtm(TimeBW) returns a spectrum object, Hs with the specified time-bandwidth product.

Hs = spectrum.mtm(DPSS,Concentrations) returns a spectrum object, Hs with the specified dpss data tapers and their concentrations.

    Note   You can either specify the time-bandwidth product (TimeBW) or the DPSS data tapers and their Concentrations. See dpss and pmtm for more information.

Hs = spectrum.mtm(...,CombineMethod) returns a spectrum object, Hs, with the specified method for combining the spectral estimates. Refer to the table above for valid CombineMethod values.

Examples

Define a cosine of 200 Hz, add noise and view its power spectral density using the Thomson multitaper algorithm with a time-bandwidth product of 3.5.

Fs=1000;
t=0:1/Fs:.3;
x=cos(2*pi*t*200)+randn(size(t));
Hs=spectrum.mtm(3.5);
psd(Hs,x,'Fs',Fs)

The above example could be done by specifying the data tapers and concentrations instead of the time-bandwidth product.

Fs=1000;
t=0:1/Fs:.3;
x=cos(2*pi*t*200)+randn(size(t));
[e,v]=dpss(length(x),3.5);
Hs=spectrum.mtm(e,v);
psd(Hs,x,'Fs',Fs)

See Also

| |

Was this topic helpful?