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iw_intel8260_localization
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matlab backup

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HappyZ 2018-02-04 18:36:19 -06:00
parent ad16d16273
commit a1642bf6ec
2 changed files with 109 additions and 2 deletions
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60
calibration_plot.m
View File

@ -1,4 +1,4 @@
clear all;
% clear all; %close all;
folder = 'calibration_data/new_indoor/';
files = dir(folder); files = files(3:end); % remove . and ..
@ -21,6 +21,8 @@ files = files(orderI);
median_result = zeros(1, length(files));
mean_result = zeros(1, length(files));
median_rssi = zeros(1, length(files));
median_diststd = zeros(1, length(files));
all_data = [];
figure(1); clf; hold on;
% figure(2); clf; hold on;
@ -56,6 +58,8 @@ for i = 1:length(files)
mean_result(i) = mean(rawDist);
median_result(i) = median(rawDist);
median_diststd(i) = median(sqrt(rawDistVar));
median_rssi(i) = median(rssi);
fprintf('distance: %d:\n', targets(i));
fprintf('* mean: %.2f (uncalibrated)\n', mean_result(i));
@ -71,7 +75,11 @@ for i = 1:length(files)
all_data = [...
all_data,...
[rawDist; targets(i) * ones(1, size(rawDist, 2))]...
[rawDist;...
targets(i) * ones(1, size(rawDist, 2));...
sqrt(rawDistVar);...
rssi
]...
];
end
@ -113,6 +121,17 @@ mstd_linear = sqrt(...
sum((data_linear - all_data(2, :)).^2) / size(all_data, 2));
scatter(all_data(1, :), data_linear, 'c.');
diffs = data_linear - all_data(2, :); diffs_std = [];
figure(4); clf; hold on;
cdfplot(diffs)
% for i = 1:size(targets, 2)
% diffs_std(i) = std(diffs(all_data(2, :) == targets(i)));
% % pause;
% end
% scatter(median_rssi, diffs_std)
figure(3);
% parabolic fit
param_parabolic = polyfit(all_data(1, :), all_data(2, :), 2);
data_parabolic = ...
@ -123,9 +142,46 @@ mstd_parabolic = sqrt(...
sum((data_parabolic - all_data(2, :)).^2) / size(all_data, 2));
scatter(all_data(1, :), data_parabolic, 'g.');
xlabel('Raw Distance');
ylabel('Target Distance');
legend('all data', 'median val', 'linear fit', 'parabolic fit', 'location', 'best')
fprintf('Std Err:\n');
fprintf(' linear mode: %.6f\n', mstd_linear);
fprintf(' parabolic mode: %.6f\n', mstd_parabolic);
%
% diffs = [];
% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706485.txt')];
% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706584.txt')];
% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706685.txt')];
% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706784.txt')];
% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706881.txt')];
% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706976.txt')];
% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517707172.txt')];
% pd = fitdist(diffs(~isnan(diffs))', 'Normal');
% prob_fit = cdf(pd, diffs(~isnan(diffs))');
% figure(10); clf; hold on;
% scatter(diffs(~isnan(diffs))', prob_fit);
% cdfplot(diffs); xlim([-200, 200])
% xlabel('Distance Err (cm)')
% title('Distribution of Distance Err When Walking')
% legend('fitted \mu=53.5043, \sigma=72.1852', 'actual dist error', 'location', 'best')
%
diffs = [];
diffs = [diffs, calibration_walking('calibration_data/walking_indoor/result_walking_3600_to_100cm_1517693155.txt')];
diffs = [diffs, calibration_walking('calibration_data/walking_indoor/result_walking_3600_to_100cm_1517693287.txt')];
diffs = [diffs, calibration_walking('calibration_data/walking_indoor/result_walking_3600_to_100cm_1517693408.txt')];
diffs = [diffs, calibration_walking('calibration_data/walking_indoor/result_walking_3600_to_100cm_1517693534.txt')];
pd = fitdist(diffs(~isnan(diffs))', 'Normal');
prob_fit = cdf(pd, diffs(~isnan(diffs))');
figure(11); clf; hold on;
scatter(diffs(~isnan(diffs))', prob_fit);
cdfplot(diffs); xlim([-200, 200])
xlabel('Distance Err (cm)')
title('Distribution of Distance Err When Walking')
legend('fitted \mu=28.8055, \sigma=50.1228', 'actual dist error', 'location', 'best')

51
calibration_walking.m Normal file
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@ -0,0 +1,51 @@
function diffs = calibration_walking(filename)
startI = 3600;
endI = 100;
if contains(filename, '_r_')
startI = 100;
endI = 3600;
end
% param_linear = [0.8927,553.3157]; % outdoor
param_linear = [0.9376, 558.0551]; % indoor
data = readtable(filename, 'ReadVariableNames', 0);
data = data(2:end, :);
caliDist = str2double(table2array(data(:, 2)))';
rawRTT = str2double(table2array(data(:, 3)))';
rawRTTVar = str2double(table2array(data(:, 4)))';
rawDist = str2double(table2array(data(:, 5)))';
rawDistVar = str2double(table2array(data(:, 6)))';
rssi = str2double(table2array(data(:, 7)))';
time = str2double(table2array(data(:, 8)))';
rawDist = movmean(rawDist, 10, 'omitnan');
rssi = movmean(rssi, 10, 'omitnan');
% for i = size(caliDist, 2) - 3: -4: 1
% rawDist(i) = nanmean(rawDist(i: i + 3));
% rawDist(i + 1: i + 3) = [];
% rssi(i) = nanmean(rssi(i: i + 3));
% rssi(i + 1: i + 3) = [];
% time(i + 1: i + 3) = [];
% end
dist = rawDist * param_linear(1) + param_linear(2);
targets = (time - time(1)) * (endI - startI) / (time(end) - time(1)) + startI;
diffs = (targets - dist);
fprintf("** mean diff: %.4f cm\n", nanmean(diffs));
fprintf("** median diff: %.4f cm\n", nanmedian(diffs));
figure(1); clf;
scatter3(time, dist, rssi); hold on; scatter(time, dist, '.')
plot([time(1), time(end)], [startI, endI]); view([0, 90]);
if contains(filename, '_r_')
title('back')
else
title('front')
end
figure(2); hold on;
pd = fitdist(diffs(~isnan(diffs))', 'Normal')
prob_fit = cdf(pd, diffs(~isnan(diffs))');
scatter(diffs(~isnan(diffs))', prob_fit);
cdfplot(diffs);
% histogram(diffs, 'BinWidth', 1, 'Normalization', 'pdf');
end