clean up

track_audio.m

@@ -15,13 +15,6 @@ simParams.fHighHz = 8000;       % Upper center frequency of the gammatone
 simParams.nChannels = 32;       % Number of filterbank channels
 simParams.frameSize = 50E-3;    % Frame size in seconds
 
-% Optional noise types
-% Choose between 'none', 'busystreet', 'restaurant', 'supermarket' and
-% 'tubestation'
-noiseType = 'tubestation';
-
-% Signal to noise ratio in dB
-snr = 0;
 
 %% Model parameters
 
@@ -46,11 +39,9 @@ addpath('./tools');
 addpath('./ekfukf-toolbox');
 
 
-%% Simulation
-
 figure(1)
 
-signal = ...
+signal = ... % get signal here
     % Get signal length and number of frames
 nSamples = size(signal, 1);
 nFrames = floor(nSamples / (simParams.frameSize * fsHz));
@@ -66,14 +57,20 @@ azimuthDistribution = ...
     squeeze(dataObj.azimuthDistribution{1}.Data(:));
 % Use -90 to align coordinate system
 measuredLocations = argmax(azimuthDistribution, 2) - 90;
+N = length(measuredLocations);
 
 % Initialize posterior mean and covariance
-posteriorMean = zeros(size(A, 1), length(measuredLocations));
-posteriorCovariance = zeros(size(A, 1), size(A, 1), ...
-    length(measuredLocations));
+posteriorMean = zeros(size(A, 1), N);
+posteriorCovariance = zeros(size(A, 1), size(A, 1), N);
 
 % Perform localization and tracking
-for l = 1 : length(measuredLocations)
+for l = 1:N
+    
+    % ===================================================
+    % Perform steps to get measurements inside this loop!
+    % ===================================================
+    
+    
     % Perform Kalman filter prediction and update
     [x, P] = kf_predict(x, P, A, Q);
     [x, P] = kf_update(x, P, measuredLocations(l), c', R);