Vibration Detection and Rejection from IMU Data

Complete MATLAB Implementation Project

Develop advanced algorithms to detect and compensate for vibrations in IMU sensor data using MATLAB!

This repository contains a complete, ready-to-run implementation of vibration detection and compensation algorithms for Inertial Measurement Units (IMUs). Perfect for students and engineers working on autonomous vehicles, drones, robotics, and navigation systems.

🚀 What You'll Build

Two-Part Implementation: Vibration Model Development - Realistic IMU sensor simulation - Multi-frequency vibration modeling - Stationary and moving trajectory generation Vibration Compensation Algorithms - 4 different filtering approaches - Real-time vibration detection - Performance analysis and comparison

📋 Requirements

MATLAB Environment

• MATLAB R2020b or later (R2023a+ recommended)
• Navigation Toolbox ✅ Required
• Signal Processing Toolbox ✅ Required
• Sensor Fusion and Tracking Toolbox ⭐ Optional but recommended

System Specs

• RAM: 4 GB minimum (8 GB recommended)
• Storage: 500 MB free space
• OS: Windows 10/11, macOS 10.15+, or Ubuntu 18.04+

🎯 Quick Start (5 Minutes!)

Step 1: Check Your Setup

% Run this verification in MATLAB
if license('test', 'Navigation_Toolbox') && license('test', 'Signal_Toolbox')
    fprintf('✅ Ready to proceed!\n');
else
    fprintf('❌ Please install required toolboxes\n');
end

Step 2: Navigate to Project

% In MATLAB, navigate to:
cd('projects/Vibration Detection and Rejection from IMU Data')

Step 3: Run the Implementation

% Part 1: Create vibration model (30 seconds)
part1_vibration_model

% Part 2: Test compensation algorithms (45 seconds)  
part2_vibration_compensation

That's it! 🎉 You now have a complete vibration detection and compensation system.

📊 What You'll Get

Immediate Results

• Real-time vibration detection with >95% accuracy
• 4 compensation algorithms compared side-by-side
• Performance metrics (RMSE, SNR, frequency analysis)
• Professional visualizations ready for presentations

Example Output

Method Performance Comparison (RMSE):
                X-axis   Y-axis   Z-axis   Average
Low-Pass:      0.1247   0.1156   0.0892   0.1098
Notch:         0.0823   0.0756   0.0634   0.0738  ← Best!
Adaptive:      0.1534   0.1423   0.1198   0.1385
Kalman:        0.1892   0.1734   0.1456   0.1694

✅ Best performing method: Notch filtering (RMSE: 0.0738 m/s²)

🔬 Technical Details

Vibration Model Features

• Multi-frequency simulation: 25Hz, 60Hz, 120Hz (motor, electrical, mechanical)
• Realistic noise characteristics: Based on commercial IMU specifications
• Trajectory support: Stationary and moving scenarios
• Configurable parameters: Easy to modify for different applications

Compensation Algorithms

1. Low-Pass Filtering - Butterworth filter for general vibration removal
2. Notch Filtering - Targeted removal of specific frequencies
3. Adaptive Filtering - Dynamic adjustment to signal conditions
4. Kalman Filtering - Optimal estimation approach

🎓 Learning Outcomes

After completing this project:

• ✅ Master IMU sensor modeling and simulation
• ✅ Understand vibration characterization techniques
• ✅ Implement advanced signal processing algorithms
• ✅ Perform quantitative performance analysis
• ✅ Apply filtering techniques to real-world problems

🔧 File Structure

📁 Vibration Detection and Rejection from IMU Data/
├── 📄 README.md                              ← Complete project guide
├── 📄 MATLAB_SETUP_GUIDE.md                  ← Detailed setup instructions
├── 📄 part1_vibration_model.m                ← Main simulation script
├── 📄 part2_vibration_compensation.m         ← Compensation algorithms
├── 📊 [Generated] imu_vibration_simulation_data.mat
├── 📊 [Generated] imu_vibration_compensation_results.mat
├── 🖼️ vibrationModel.png                     ← Reference diagram
└── 🖼️ VibrationCompensation.png             ← Reference diagram

🌟 Industry Applications

This implementation is directly applicable to:

• Autonomous Vehicles - Robust navigation in vibrating environments
• Drone Systems - Stable flight control despite motor vibrations
• Robotics - Accurate sensing for mobile robots
• Aerospace - Guidance systems for aircraft and spacecraft
• Industrial IoT - Vibration monitoring and predictive maintenance

🚀 Advanced Extensions

Ready for More?

1. Hardware Integration - Connect real IMU sensors via Arduino
2. Machine Learning - Implement neural network-based detection
3. Real-time Processing - Stream data from mobile devices
4. Multi-sensor Fusion - Combine multiple IMUs for redundancy

Extension Code Examples

% Real-time data streaming (requires MATLAB Mobile)
m = mobiledev;
accel_data = accellog(m);  % Live accelerometer data

% Machine learning vibration classifier  
net = trainNetwork(features, labels, layers, options);
vibration_detected = classify(net, current_features);

📚 Educational Value

Perfect for:

• Engineering Coursework - Signal processing, control systems, robotics
• Research Projects - Navigation, sensor fusion, autonomous systems
• Industry Training - IMU applications, filtering techniques
• Competition Preparation - Robotics contests, autonomous challenges

Skill Level: Suitable for Bachelor's through Doctoral level

🆘 Need Help?

Quick Solutions:

• Setup Issues? → See MATLAB_SETUP_GUIDE.md
• Script Errors? → Check toolbox installation with ver command
• Performance Issues? → Close other applications, reduce simulation time
• Can't Find Files? → Ensure you're in the correct project directory

Resources:

• MathWorks Documentation: Navigation Toolbox | Signal Processing
• Technical Support: MathWorks Support
• Community: MATLAB Central

📈 Project Impact

Real-World Impact: Improve navigation systems by making them robust against vibrations - enabling safer autonomous vehicles, more stable drones, and more accurate robotic systems.

Skills Gained:

• Advanced MATLAB programming
• Digital signal processing expertise
• IMU sensor understanding
• Algorithm performance analysis
• Engineering problem-solving

📝 Project Registration

Want official recognition for your work?

Fill out this registration form to register your intent and receive certificates upon completion.

Fill out this submission form to submit your completed project for recognition and rewards.

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🎉 Ready to Get Started?

1. ✅ Verify your MATLAB setup has the required toolboxes
2. 📂 Navigate to the project folder
3. 🏃 Run part1_vibration_model followed by part2_vibration_compensation
4. 📈 Analyze your results and explore the generated visualizations
5. 🚀 Extend the implementation with your own innovations!

Estimated Time: 2-4 hours for complete implementation and analysis

Questions? Check the detailed project README for comprehensive guidance.

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Transform vibrating IMU data into clean, reliable sensor measurements!
A complete MATLAB implementation ready for real-world applications.