From 1390b13132b84c1f1eb857649d7e7ad7fb8b310e Mon Sep 17 00:00:00 2001
From: Stefano Belletti <stefano.belletti@skywarder.eu>
Date: Fri, 17 Jan 2025 18:14:31 +0100
Subject: [PATCH] Final implementation of colored noise
---
.../Functions/NoiseAnalysis_colored.m | 6 +--
.../Functions/NoiseAnalysis_pink.m | 6 +--
.../NoiseAnalysis/main_noiseAnalysis.m | 2 +-
commonFunctions/sensors/Sensor1D.m | 19 ++++++---
commonFunctions/sensors/Sensor3D.m | 37 ++++++++++++++----
.../Lyra_Port_sensor_vect_res.mat | Bin 4452 -> 4423 bytes
6 files changed, 50 insertions(+), 20 deletions(-)
diff --git a/commonFunctions/sensors/NoiseAnalysis/Functions/NoiseAnalysis_colored.m b/commonFunctions/sensors/NoiseAnalysis/Functions/NoiseAnalysis_colored.m
index d525ae5..f285150 100644
--- a/commonFunctions/sensors/NoiseAnalysis/Functions/NoiseAnalysis_colored.m
+++ b/commonFunctions/sensors/NoiseAnalysis/Functions/NoiseAnalysis_colored.m
@@ -87,15 +87,15 @@ if ~plot_val
end
switch track
case 2
- sensor_vect(sensor_number).track1.white_variance = white_variance*sensor_vect(sensor_number).factor;
+ sensor_vect(sensor_number).track1.white_variance = white_variance;
sensor_vect(sensor_number).track1.fcut = fcut;
sensor_vect(sensor_number).track1.butterOrder = butterOrder;
case 3
- sensor_vect(sensor_number).track2.white_variance = white_variance*sensor_vect(sensor_number).factor;
+ sensor_vect(sensor_number).track2.white_variance = white_variance;
sensor_vect(sensor_number).track2.fcut = fcut;
sensor_vect(sensor_number).track2.butterOrder = butterOrder;
case 4
- sensor_vect(sensor_number).track3.white_variance = white_variance*sensor_vect(sensor_number).factor;
+ sensor_vect(sensor_number).track3.white_variance = white_variance;
sensor_vect(sensor_number).track3.fcut = fcut;
sensor_vect(sensor_number).track3.butterOrder = butterOrder;
end
diff --git a/commonFunctions/sensors/NoiseAnalysis/Functions/NoiseAnalysis_pink.m b/commonFunctions/sensors/NoiseAnalysis/Functions/NoiseAnalysis_pink.m
index 9daf7a7..16acacc 100644
--- a/commonFunctions/sensors/NoiseAnalysis/Functions/NoiseAnalysis_pink.m
+++ b/commonFunctions/sensors/NoiseAnalysis/Functions/NoiseAnalysis_pink.m
@@ -139,15 +139,15 @@ if ~plot_val
case 2
sensor_vect(sensor_number).track1.peaks_vect_f = peaks_vect_f;
sensor_vect(sensor_number).track1.peaks_vect_val = factor*peaks_vect_val;
- sensor_vect(sensor_number).track1.variance = white_variance*sensor_vect(sensor_number).factor;
+ sensor_vect(sensor_number).track1.variance = white_variance;
case 3
sensor_vect(sensor_number).track2.peaks_vect_f = peaks_vect_f;
sensor_vect(sensor_number).track2.peaks_vect_val = factor*peaks_vect_val;
- sensor_vect(sensor_number).track2.variance = white_variance*sensor_vect(sensor_number).factor;
+ sensor_vect(sensor_number).track2.variance = white_variance;
case 4
sensor_vect(sensor_number).track3.peaks_vect_f = peaks_vect_f;
sensor_vect(sensor_number).track3.peaks_vect_val = factor*peaks_vect_val;
- sensor_vect(sensor_number).track3.variance = white_variance*sensor_vect(sensor_number).factor;
+ sensor_vect(sensor_number).track3.variance = white_variance;
end
end
diff --git a/commonFunctions/sensors/NoiseAnalysis/main_noiseAnalysis.m b/commonFunctions/sensors/NoiseAnalysis/main_noiseAnalysis.m
index 87c4405..b0cecd7 100644
--- a/commonFunctions/sensors/NoiseAnalysis/main_noiseAnalysis.m
+++ b/commonFunctions/sensors/NoiseAnalysis/main_noiseAnalysis.m
@@ -5,7 +5,7 @@ restoredefaultpath
%% Path
-matFolder = "";
+matFolder = "C:\Users\stebe\Desktop\New data";
addpath(genpath("./Functions"))
addpath(genpath(matFolder))
diff --git a/commonFunctions/sensors/Sensor1D.m b/commonFunctions/sensors/Sensor1D.m
index 8c7f226..dd2fcc5 100644
--- a/commonFunctions/sensors/Sensor1D.m
+++ b/commonFunctions/sensors/Sensor1D.m
@@ -21,6 +21,7 @@ classdef Sensor1D < handle
noiseDataTrack1;
noiseFactor;
colored_opts;
+
noiseVariance; % Defining gaussian white noise
% offset
@@ -100,11 +101,13 @@ classdef Sensor1D < handle
error("Sensor not defined")
end
- if strcmp(obj.noiseType, "colored")
+ if strcmp(obj.noiseType, "colored") || strcmp(obj.noiseType, "pink")
obj.colored_opts.white_variance = vect(ii).colored_data.white_variance;
obj.colored_opts.fcut = vect(ii).colored_data.fcut;
obj.colored_opts.butterOrder = vect(ii).colored_data.butterOrder;
- obj.colored_opts.filterStatus = 0;
+ obj.colored_opts.filterStatus1 = 0;
+ obj.colored_opts.filterStatus2 = 0;
+ obj.colored_opts.filterStatus3 = 0;
end
else
if strcmp("Sensor1D", class(obj)) || strcmp("SensorFault", class(obj))
@@ -156,11 +159,15 @@ classdef Sensor1D < handle
for ii = 1:length(obj.noiseDataTrack1.peaks_vect_f)
inputArg = inputArg + obj.noiseDataTrack1.peaks_vect_val(ii)*obj.noiseFactor*sin(2*pi*obj.noiseDataTrack1.peaks_vect_f(ii)*t + randn(1));
end
- inputArg = inputArg + sqrt(obj.noiseDataTrack1.variance*obj.noiseFactor).*randn(length(inputArg),1);
- elseif strcmp(obj.noiseType, "colored")
- inputArg = inputArg + sqrt(obj.colored_opts.white_variance*obj.noiseFactor).*randn(length(inputArg),1);
+ % Colored noise
+ white_noise = sqrt(obj.noiseDataTrack1.variance*obj.noiseFactor).*randn(length(inputArg),1);
[b, a] = butter(obj.colored_opts.butterOrder, obj.colored_opts.fcut, 'low');
- [inputArg, obj.colored_opts.filterStatus] = filter(b, a, inputArg, obj.colored_opts.filterStatus);
+ [colored_noise, obj.colored_opts.filterStatus1] = filter(b, a, white_noise, obj.colored_opts.filterStatus1);
+ inputArg = inputArg + colored_noise;
+ elseif strcmp(obj.noiseType, "colored")
+ inputArg = inputArg + sqrt(obj.noiseDataTrack1.white_variance*obj.noiseFactor).*randn(length(inputArg),1);
+ [b, a] = butter(obj.noiseDataTrack1.butterOrder, obj.noiseDataTrack1.fcut, 'low');
+ [inputArg, obj.colored_opts.filterStatus1] = filter(b, a, inputArg, obj.colored_opts.filterStatus1);
else
error("This noise is not defined")
end
diff --git a/commonFunctions/sensors/Sensor3D.m b/commonFunctions/sensors/Sensor3D.m
index bada245..5126456 100644
--- a/commonFunctions/sensors/Sensor3D.m
+++ b/commonFunctions/sensors/Sensor3D.m
@@ -135,15 +135,38 @@ classdef Sensor3D < Sensor1D
inputArg3 = inputArg3 + sqrt(obj.noiseDataTrack3*obj.noiseFactor).*randn(length(inputArg3),1);
elseif strcmp(obj.noiseType, "pink")
for ii = 1:length(obj.noiseDataTrack1.peaks_vect_f)
- inputArg1 = inputArg1 + obj.noiseDataTrack1.peaks_vect_val(ii)*obj.noiseFactor*sin(2*pi*obj.noiseDataTrack1.peaks_vect_f(ii)*t + randn(1));
- inputArg2 = inputArg2 + obj.noiseDataTrack2.peaks_vect_val(ii)*obj.noiseFactor*sin(2*pi*obj.noiseDataTrack2.peaks_vect_f(ii)*t + randn(1));
- inputArg3 = inputArg3 + obj.noiseDataTrack3.peaks_vect_val(ii)*obj.noiseFactor*sin(2*pi*obj.noiseDataTrack3.peaks_vect_f(ii)*t + randn(1));
+ inputArg1 = inputArg1 + obj.noiseDataTrack1.peaks_vect_val(ii)*sin(2*pi*obj.noiseDataTrack1.peaks_vect_f(ii)*t + randn(1));
+ inputArg2 = inputArg2 + obj.noiseDataTrack2.peaks_vect_val(ii)*sin(2*pi*obj.noiseDataTrack2.peaks_vect_f(ii)*t + randn(1));
+ inputArg3 = inputArg3 + obj.noiseDataTrack3.peaks_vect_val(ii)*sin(2*pi*obj.noiseDataTrack3.peaks_vect_f(ii)*t + randn(1));
end
- inputArg1 = inputArg1 + sqrt(obj.noiseDataTrack1.variance*obj.noiseFactor).*randn(length(inputArg1),1);
- inputArg2 = inputArg2 + sqrt(obj.noiseDataTrack2.variance*obj.noiseFactor).*randn(length(inputArg2),1);
- inputArg3 = inputArg3 + sqrt(obj.noiseDataTrack3.variance*obj.noiseFactor).*randn(length(inputArg3),1);
+ % Colored noise
+ white_noise1 = sqrt(obj.noiseDataTrack1.variance*obj.noiseFactor).*randn(1,1);
+ [b1, a1] = butter(obj.noiseDataTrack1.butterOrder, obj.noiseDataTrack1.fcut, 'low');
+ [colored_noise1, obj.colored_opts.filterStatus1] = filter(b1, a1, white_noise1, obj.colored_opts.filterStatus1);
+ white_noise2 = sqrt(obj.noiseDataTrack2.variance*obj.noiseFactor).*randn(1,1);
+ [b2, a2] = butter(obj.noiseDataTrack2.butterOrder, obj.noiseDataTrack2.fcut, 'low');
+ [colored_noise2, obj.colored_opts.filterStatus2] = filter(b2, a2, white_noise2, obj.colored_opts.filterStatus2);
+ white_noise3 = sqrt(obj.noiseDataTrack3.variance*obj.noiseFactor).*randn(1,1);
+ [b3, a3] = butter(obj.noiseDataTrack3.butterOrder, obj.noiseDataTrack3.fcut, 'low');
+ [colored_noise3, obj.colored_opts.filterStatus3] = filter(b3, a3, white_noise3, obj.colored_opts.filterStatus3);
+
+ inputArg1 = inputArg1 + colored_noise1;
+ inputArg2 = inputArg2 + colored_noise2;
+ inputArg3 = inputArg3 + colored_noise3;
elseif strcmp(obj.noiseType, "colored")
- error("Yet to be implemented")
+ white_noise1 = sqrt(obj.noiseDataTrack1.variance*obj.noiseFactor).*randn(1,1);
+ [b1, a1] = butter(obj.noiseDataTrack1.butterOrder, obj.noiseDataTrack1.fcut, 'low');
+ [colored_noise1, obj.colored_opts.filterStatus1] = filter(b1, a1, white_noise1, obj.colored_opts.filterStatus1);
+ white_noise2 = sqrt(obj.noiseDataTrack2.variance*obj.noiseFactor).*randn(1,1);
+ [b2, a2] = butter(obj.noiseDataTrack2.butterOrder, obj.noiseDataTrack2.fcut, 'low');
+ [colored_noise2, obj.colored_opts.filterStatus2] = filter(b2, a2, white_noise2, obj.colored_opts.filterStatus2);
+ white_noise3 = sqrt(obj.noiseDataTrack3.variance*obj.noiseFactor).*randn(1,1);
+ [b3, a3] = butter(obj.noiseDataTrack3.butterOrder, obj.noiseDataTrack3.fcut, 'low');
+ [colored_noise3, obj.colored_opts.filterStatus3] = filter(b3, a3, white_noise3, obj.colored_opts.filterStatus3);
+
+ inputArg1 = inputArg1 + colored_noise1;
+ inputArg2 = inputArg2 + colored_noise2;
+ inputArg3 = inputArg3 + colored_noise3;
else
error("This noise is not defined")
end
diff --git a/data/2024_Lyra_Portugal_October/Lyra_Port_sensor_vect_res.mat b/data/2024_Lyra_Portugal_October/Lyra_Port_sensor_vect_res.mat
index 2c3b98ad9def8becb8ced991dfe7f38b560308d0..0b806e6b498b7f8c1b7a91c4cc43ae2c1a5c0a80 100644
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--
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