Removing drift from noisy accelerometer data

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Hi all,
I am using the sensors within my phone to generate a CSV file of accelerations in 3-axis (x,y,z). I have now imported the data to matlab using the CSVread funtion and have began processing the data.
I have applied a filter to reduce some of the noise from the signal however upon integration the signal still drifts. The code I am using is shown below, for simplicitys sake I am only showing data from one axis. Any help is appreciated
clear; close; clc;
D=csvread('test20m3.csv');
t=D(:,1); %Define time
XAccRaw=D(:,5); %Define X acceleration
XAcc=XAccRaw*9.81; %Convert to m/s^2
d=designfilt('lowpassfir','filterorder',10,'CutOffFrequency',10,'SampleRate',100); %Lowpass FIR filter
AX=filtfilt(d,XAcc); %Apply filter to data
VX=cumtrapz(t,AX); %Integrate acceleration to get velocity
SX=cumtrapz(t,VX); %Integrate velocity to get displacement
figure(1);
plot(t,SX);
xlabel(Time (s));
ylabel(Displacement (m))

回答(2 个)

Image Analyst
Image Analyst 2020-4-14
I think this works great. It's well commented, practically self documenting but if you have any questions, ask.
% Initialization steps.
clc; % Clear the command window.
fprintf('Beginning to run %s.m ...\n', mfilename);
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
% Load data and plot it.
hFig1 = figure;
sa = load('acceleration.mat')
accel = sa.acceleration1;
st = load('time.mat')
t = st.time1;
plot(t, accel, 'b.-', 'MarkerSize', 9);
grid on;
hold on;
fontSize = 20;
xlabel('Time', 'FontSize', fontSize);
ylabel('Acceleration', 'FontSize', fontSize);
title('Original Signal', 'FontSize', fontSize);
hFig1.WindowState = 'maximized'; % Maximize the figure window.
% Draw a line at y=0
yline(0, 'LineWidth', 2);
% A moving trend is influenced by the huge outliers, so get rid of those first.
% Find outliers
outlierIndexes = isoutlier(accel);
plot(t(outlierIndexes), accel(outlierIndexes), 'ro', 'MarkerSize', 15);
% Extract the good data.
tGood = t(~outlierIndexes);
accelGood = accel(~outlierIndexes);
% plot(t(~outlierIndexes), accel(~outlierIndexes), 'mo', 'MarkerSize', 10); % Plot circles around the good data.
% Do a Savitzky-Golay filter (moving quadratic).
windowWidth = 51; % Smaller for tighter following of original data, bigger for smoother curve.
smoothedy = sgolayfilt(accelGood, 2, windowWidth);
hold on;
plot(tGood, smoothedy, 'r-', 'LineWidth', 2);
legend('Original Signal', 'X axis', 'Outliers', 'Smoothed Signal');
% Now it looks pretty reasonable since we didn't include the outliers.
% But smoothedy has fewer points so if we're to subtract it from the original
% we have to fill in the missing points.
smoothedy = interp1(tGood, smoothedy, t);
% Now subtract the smoothed signal to get the variation
signal = accel - smoothedy;
% Plot it.
hFig2 = figure;
plot(t, signal, 'b.-', 'MarkerSize', 9);
grid on;
hold on;
title('Corrected Signal', 'FontSize', fontSize);
xlabel('Time', 'FontSize', fontSize);
ylabel('Acceleration', 'FontSize', fontSize);
hFig2.Units = 'normalized';
hFig2.Position = [.2, .2, .5, .5]; % Size the figure window.
% Draw a line at y=0
yline(0, 'LineWidth', 2);
  10 个评论
Image Analyst
Image Analyst 2023-4-7
Same reply (not sure why you ignored it). Start a new discussion thread and attach your data and code, especially the code to turn acceleration into displacement.

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Ameer Hamza
Ameer Hamza 2020-4-13
If by drift, you mean the signal continually move away from the actual value then you can try to use detrend() function: https://www.mathworks.com/help/matlab/ref/detrend.html
  12 个评论
Jack Upton
Jack Upton 2020-4-18
I thought that would be the case as thats what I have read elsewhere,Ive tried to implement it previously using GPS as well as my accelerometer results but I have had no luck
Ameer Hamza
Ameer Hamza 2020-4-18
Yes, it can be a bit complicated because you need to estimate the model of your system. You can try to follow some example on Kalman filter in MATLAB: https://www.mathworks.com/discovery/kalman-filter.html

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