6 plot_ beam

Purpose

Displays the directivity pattern and optionally the learning curve for evaluating the performance of beam former applications.

Syntax

plot_beam(E, w, L, Wo, cT)
plot_beam(E, w, L, Wo, cT, lc)

Description

plot_beam() displays the learning curve and directivity pattern for evaluating the performance of an adaptive array (beam former). plot_beam() takes as input the mean square error vector $E(n)$ (returned by update_ipwin()), the adaptive filter coefficients vector $w(n)$, the array distance vector $L$, the radian frequency $W_o$, and the product term $cT$, and returns after rendering the graphs. If the last input argument $lc$ is given and equals to one, only the directivity pattern is plotted. The variables of plot_beam() are summarized below.

 
Input variables:
  E  : mean square error
  w  : filter coefficients vector
  L  : vector containing distances between array elements
  Wo : sampled radian frequency
  cT : product of propagation speed * sampling period
  lc : if 1 will plot the directivity pattern only.

Example

Figure 9.5: The adaptive beam former graph window.

An example mean former graph window generated using plot_beam() is shown in Fig. 9.5. The left panel in this graph displays the learning curve of the adaptive array and the right panel displays the directivity pattern.