Accelerating the pace of engineering and science

# Documentation Center

• Trial Software

# sigwin.kaiser class

Package: sigwin

Construct Kaiser window object

## Description

sigwin.kaiser creates a handle to a Kaiser window object for use in spectral analysis and FIR filtering by the window method. Object methods enable workspace import and ASCII file export of the window values.

The following equation defines the Kaiser window of length N:

where x is linearly spaced N-point vector and is the modified zero-th order Bessel function of the first kind. is the attenuation parameter.

## Construction

H = sigwin.kaiser returns a Kaiser window object H of length 64 and attenuation parameter beta of 0.5.

H = sigwin.kaiser(Length) returns a Kaiser window object H of length Length and attenuation parameter beta of 0.5. Length requires a positive integer. Entering a positive noninteger value for Length rounds the length to the nearest integer. Entering a 1 for Length results in a window with a single value of 1.

H = sigwin.kaiser(Length,Beta) returns a Kaiser window object with real-valued attenuation parameter beta.

## Properties

 Length Kaiser window length. The window length requires a positive integer. Entering a positive noninteger value for Length rounds the length to the nearest integer. Entering a 1 for Length results in a window with a single value of 1. Beta Attenuation parameter. Beta requires a real number. Larger absolute values of Beta result in greater stopband attenuation, or equivalently greater attenuation between the main lobe and first side lobe.

## Methods

 generate Generates Kaiser window info Display information about Kaiser window object winwrite Save Kaiser window in ASCII file

## Copy Semantics

Handle. To learn how copy semantics affect your use of the class, see Copying Objects in the MATLAB® Programming Fundamentals documentation.

## Examples

Compare two Kaiser windows with different Beta values:

H = sigwin.kaiser(128,1.5);
% Kaiser window with Beta=4.5
H1 = sigwin.kaiser(128,4.5);
% Plot comparison
fwvt = wvtool(H,H1);
legend(get(fwvt,'currentaxes'),'\beta=1.5','\beta=4.5');


## References

Oppenheim, A.V., and Schafer, R.W. Discrete-time Signal Processing, Upper Saddle River, N.J: Prentice Hall, 1989, pp. 444–447.