Commit c63be83a authored by Fredrik Bagge Carlsson's avatar Fredrik Bagge Carlsson
Browse files

tr back

parent cb1df5f1
clear;
close all;
clc;
fs = 24414;
chunks = 2048;
dt = chunks/fs;
[Bf, Af] = butter(3,8000/fs); % filter vectors
%% Setup objects
% Initialize localization models using braodband and subband settings
dObj = dataObject([],fs,10,2);
% Settings for subband approach
par_sub = genParStruct('cc_bBroadband',0,'cc_wSizeSec',winSec,...
'cc_hSizeSec',winSec/2,'cc_maxDelaySec',1.25e-3,...
'fb_lowFreqHz',fLowHz,'fb_highFreqHz',fHighHz,...
'fb_nERBs',1,'ihc_method','none',...
'loc_NSources',nSpeakers(hh));
% Initialize localization models using braodband and subband settings
mObj = manager(dObj,'localization',par_sub);
%% Model parameters
sigma_w = 1;
Q = [2/4*dt^4, 1/2*dt^3; 1/2*dt^3, dt^2]*sigma_w; % Process noise covariance
R = 1; % Measurement noise covariance
x = [0; 0]; % Initial state
P = [10, 0; 0, 10]; % Initial state covariance
A = [1, dt; 0, 1]; % System matrix
c = [1; 0]; % Output vector
% Check definiteness of covariance matrices
if ~all(eig(Q) > 0) || ~all(eig(R) > 0) || ~all(eig(A) > 0)
error('All covariance matrices have to be positive definite.');
end
%% Initialization
% Add necessary paths
addpath('./tools');
addpath('./ekfukf-toolbox');
figure(1)
N = 1; % The number of steps to run this stuff.
% Initialize posterior mean and covariance
posteriorMean = zeros(size(A, 1), N);
posteriorCovariance = zeros(size(A, 1), size(A, 1), N);
% =======================================================
% Main loop - Perform localization and tracking
% =======================================================
tic();
t_old = toc();
for l = 1:N
audio = get_audio();
t_new = toc();
dti = t_new - t_old();
% Request processing
mObj.processSignal(audio);
azimEst = dObj.localization{1}.Data(end,1); % There might be an issue with several sources here!
% Perform Kalman filter prediction and update, TODO: consider changing this
% crappy filter for a PF
Qi = [1/4*dti^4+1e-6, 1/2*dti^3; 1/2*dti^3, dti^2]*sigma_w; % Process noise covariance
Ai = [1, dti; 0, 1];
[x, P] = kf_predict(x, P, Ai, Qi);
[x, P] = kf_update(x, P, azimEst, c', R);
posteriorMean(:, l) = x;
posteriorCovariance(:, :, l) = P;
%pause(max0))
t_old = t_new;
end
% Plot measurements
subplot(2, nFiles / 2, k);
timeAxis = linspace(0, nSamples / fsHz, nFrames);
plot(timeAxis, measuredLocations, 'x', 'LineWidth', 2);
axis([0, nSamples / fsHz, -90, 90]);
xlabel('Time / s');
ylabel('Azimuth / deg');
grid on; hold on;
plot(timeAxis, posteriorMean(1, :), 'g', 'LineWidth', 2);
% Plot ground truth
plot(timeAxis, gtTrajectory, 'r--', 'LineWidth', 2);
legend('Measurements', 'Estimated trajectory', 'Ground truth');
% Compute RMSE
rmse = sqrt(sum((posteriorMean(1, :) - gtTrajectory).^2) ./ nFrames);
if ~strcmpi(noiseType, 'none')
title([upper(soundType), ', ', upper(noiseType), ' NOISE AT ', ...
num2str(snr), ' dB SNR, ', 'RMSE: ', num2str(rmse), '°']);
else
title([upper(soundType), ', NO NOISE, ', 'RMSE: ', ...
num2str(rmse), '°']);
end
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