In B0_P1 dataset, first floor antenna array graph is quite different with second floor antenna array graph #17
Description
Hi,
I am studying this work, and I found something strange:
In data B0_P1, If we use 45 channel which from antenna 1# to 10# in DMAS, we get following graph, we can found a focus point in (0,0.02)
but, If we use 45 channel from antenna 11# to 20# in DMAS, we found a focus point in (-0.02,-0.02)
This situation just happened in this case , I am not sure why , maybe you will be interested?
The 24 antenna DAS graph is here , we found the focus point in (-0.02,-0.02)
So, why does the graph in 1st floor is quite different with 2nd floor?
This is quite interesting right?
I put my test code below:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
close all;
clc;
%% Load sample data (antenna locations, frequencies and signals)
frequencies = dlmread('example_data/frequencies.csv');
antenna_locations = dlmread('example_data/antenna_locations.csv');
channel_names = dlmread('example_data/channel_names.csv');
scan1 = dlmread('example_data/B0_P1_p000.csv');
scan2 = dlmread('example_data/B0_P1_p036.csv');
%% channel filter
[data_count,~] = size(channel_names);
condition = ones(data_count,1);
% ant_list =[1,2,3,4,5,6,7,8,9,10];
x1 = 11;
x2 = 20;
ant_list =linspace(x1,x2,x2-x1 + 1);
for i = 1 : length(channel_names)
if(any((channel_names(i,1) == ant_list)) )|| (any((channel_names(i,2) == ant_list)) )
condition(i,:) = 0;
end
end
%
channel_names = channel_names(~condition, :);
scan1 = scan1(:, ~condition);
scan2 = scan2(:, ~condition);
%% Perform rotation subtraction
signals = scan1-scan2;
%% Generate imaging domain
[points, axes_] = merit.domain.hemisphere( 7e-2, 'resolution', 2.5e-3);
%% Calculate delays for synthetic focusing
delays = merit.beamform.get_delays(channel_names, antenna_locations,'relative_permittivity', 8);
%% Perform imaging
img = abs(merit.beamform(signals, frequencies, points, delays,merit.beamformers.DAS));
% img = abs(merit.beamform(signals, frequencies, points, delays, merit.beamformers.DMAS));
% img = abs(merit.beamform(signals, frequencies, points, delays, merit.beamformers.CDAS));
%% Plot image using MATLAB functions
im_slice = merit.visualize.get_slice(img, points, axes_, 'z', 35e-3);
imagesc(axes_{1:2}, im_slice);