187 lines
6.8 KiB
Matlab
187 lines
6.8 KiB
Matlab
% clear all; %close all;
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folder = 'calibration_data/new_indoor/';
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files = dir(folder); files = files(3:end); % remove . and ..
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targets = zeros(1, length(files));
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for i = length(files):-1:1
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if (~contains(files(i).name, 'result') ||...
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contains(files(i).name, 'left') ||...
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contains(files(i).name, 'right') ||...
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contains(files(i).name, 'ap') ||...
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contains(files(i).name, 'down') ||...
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contains(files(i).name, 'up'))
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files(i) = [];
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targets(i) = [];
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else
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targets(i) = sscanf(files(i).name, 'result_%dcm.txt');
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end
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end
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[targets, orderI] = sort(targets);
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files = files(orderI);
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median_result = zeros(1, length(files));
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mean_result = zeros(1, length(files));
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median_rssi = zeros(1, length(files));
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median_diststd = zeros(1, length(files));
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all_data = [];
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figure(1); clf; hold on;
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% figure(2); clf; hold on;
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for i = 1:length(files)
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filename = [folder, files(i).name];
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fileID = fopen(filename, 'r');
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formatSpec = [...
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'Target: %x:%x:%x:%x:%x:%x, status: %d, ',...
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'rtt: %d psec, distance: %d cm\n'...
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];
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data = fscanf(fileID, formatSpec, [9 Inf]);
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fclose(fileID);
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if isempty(data)
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data = readtable(filename, 'ReadVariableNames', 0);
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if isempty(data)
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continue
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end
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data = data(2:end, :);
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caliDist = str2double(table2array(data(:, 2)))';
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rawRTT = str2double(table2array(data(:, 3)))';
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rawRTTVar = str2double(table2array(data(:, 4)))';
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rawDist = str2double(table2array(data(:, 5)))';
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rawDistVar = str2double(table2array(data(:, 6)))';
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rssi = str2double(table2array(data(:, 7)))';
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time = str2double(table2array(data(:, 8)))';
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else
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% get rid of invalid data
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data(:, data(7, :) ~= 0) = [];
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data(:, data(9, :) < -1000) = [];
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rawDist = data(9, :);
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end
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mean_result(i) = mean(rawDist);
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median_result(i) = median(rawDist);
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median_diststd(i) = median(sqrt(rawDistVar));
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median_rssi(i) = median(rssi);
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fprintf('distance: %d:\n', targets(i));
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fprintf('* mean: %.2f (uncalibrated)\n', mean_result(i));
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fprintf('* median: %.2f (uncalibrated)\n', median_result(i));
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fprintf('* std: %.2f (uncalibrated)\n', std(rawDist));
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figure(1); cdfplot(rawDist);
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% figure(2);
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% scatter3(...
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% sqrt(rawDistVar),...
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% rssi,...
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% rawDist - targets(i));
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all_data = [...
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all_data,...
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[rawDist;...
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targets(i) * ones(1, size(rawDist, 2));...
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sqrt(rawDistVar);...
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rssi
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]...
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];
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end
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% % shuffle
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% shuffled_data = all_data(:, randperm(size(all_data, 2)));
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%
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% % 10-fold cross validation
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% step = floor(size(shuffled_data, 2) / 20);
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% params = zeros(2, 20);
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% mse = zeros(1, 20);
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% for i = 1:20
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% from = step * (i - 1) + 1;
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% to = step * i;
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% train_data = shuffled_data;
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% test_data = train_data(:, from:to);
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% train_data(:, from:to) = [];
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% params(:, i) = polyfit(train_data(1, :), train_data(2, :), 1);
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% test_est = params(1, i) * test_data(1, :) + params(2, i);
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% mse(i) = sum((test_est - test_data(2, :)).^2) / size(test_data, 2);
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% end
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% param(1) = sum(params(1, :)) / size(mse, 2);
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% % mse ./ sum(mse) * params(1, :)';
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% param(2) = sum(params(2, :)) / size(mse, 2);
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% % mse ./ sum(mse) * params(2, :)';
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% validated_fit_data = param(1) * all_data(1, :) + param(2);
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% mstd_1 = sqrt(sum((validated_fit_data - all_data(2, :)).^2) /...
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% size(all_data, 2));
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figure(3); clf; hold on;
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scatter(all_data(1, :), all_data(2, :), 'b.');
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plot(median_result, targets, 'r', 'LineWidth', 2)
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% linear fit
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param_linear = polyfit(all_data(1, :), all_data(2, :), 1);
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data_linear = param_linear(1) * all_data(1, :) + param_linear(2);
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mstd_linear = sqrt(...
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sum((data_linear - all_data(2, :)).^2) / size(all_data, 2));
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scatter(all_data(1, :), data_linear, 'c.');
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diffs = data_linear - all_data(2, :); diffs_std = [];
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figure(4); clf; hold on;
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cdfplot(diffs)
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% for i = 1:size(targets, 2)
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% diffs_std(i) = std(diffs(all_data(2, :) == targets(i)));
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% % pause;
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% end
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% scatter(median_rssi, diffs_std)
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figure(3);
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% parabolic fit
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param_parabolic = polyfit(all_data(1, :), all_data(2, :), 2);
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data_parabolic = ...
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param_parabolic(1) * all_data(1, :).^2 +...
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param_parabolic(2) * all_data(1, :) + ...
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param_parabolic(3);
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mstd_parabolic = sqrt(...
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sum((data_parabolic - all_data(2, :)).^2) / size(all_data, 2));
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scatter(all_data(1, :), data_parabolic, 'g.');
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xlabel('Raw Distance');
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ylabel('Target Distance');
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legend('all data', 'median val', 'linear fit', 'parabolic fit', 'location', 'best')
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fprintf('Std Err:\n');
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fprintf(' linear mode: %.6f\n', mstd_linear);
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fprintf(' parabolic mode: %.6f\n', mstd_parabolic);
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%
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% diffs = [];
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% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706485.txt')];
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% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706584.txt')];
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% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706685.txt')];
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% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706784.txt')];
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% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706881.txt')];
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% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517706976.txt')];
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% diffs = [diffs, calibration_walking('calibration_data/walking_outdoor/result_walking_3800_to_100cm_1517707172.txt')];
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% pd = fitdist(diffs(~isnan(diffs))', 'Normal');
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% prob_fit = cdf(pd, diffs(~isnan(diffs))');
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% figure(10); clf; hold on;
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% scatter(diffs(~isnan(diffs))', prob_fit);
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% cdfplot(diffs); xlim([-200, 200])
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% xlabel('Distance Err (cm)')
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% title('Distribution of Distance Err When Walking')
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% legend('fitted \mu=53.5043, \sigma=72.1852', 'actual dist error', 'location', 'best')
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%
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diffs = [];
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diffs = [diffs, calibration_walking('calibration_data/walking_indoor/result_walking_3600_to_100cm_1517693155.txt')];
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diffs = [diffs, calibration_walking('calibration_data/walking_indoor/result_walking_3600_to_100cm_1517693287.txt')];
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diffs = [diffs, calibration_walking('calibration_data/walking_indoor/result_walking_3600_to_100cm_1517693408.txt')];
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diffs = [diffs, calibration_walking('calibration_data/walking_indoor/result_walking_3600_to_100cm_1517693534.txt')];
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pd = fitdist(diffs(~isnan(diffs))', 'Normal');
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prob_fit = cdf(pd, diffs(~isnan(diffs))');
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figure(11); clf; hold on;
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scatter(diffs(~isnan(diffs))', prob_fit);
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cdfplot(diffs); xlim([-200, 200])
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xlabel('Distance Err (cm)')
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title('Distribution of Distance Err When Walking')
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legend('fitted \mu=28.8055, \sigma=50.1228', 'actual dist error', 'location', 'best') |