Diploma in IMU/Gyros, Fusion and Calibration
Online postgraduate diploma of 800 hours in IMU/gyros, sensor fusion and calibration. 8 modules covering MEMS inertial sensors, error characterisation, IMU calibration, attitude representation, sensor fusion filters, embedded implementation and validation, with an assigned technical tutor.
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What you will be able to do
Characterise inertial sensor errors using Allan variance.
Calibrate accelerometers, gyroscopes and magnetometers with multi-position procedures.
Represent attitude with quaternions and rotation matrices.
Design complementary and Kalman fusion filters to estimate attitude.
Implement fusion algorithms on real-time microcontrollers.
Validate an AHRS against higher-accuracy references.
Integrate IMUs with other navigation sources in real applications.
Who it's for
- Inertial sensor engineers working on characterisation and calibration.
- Navigation engineers building attitude and heading solutions.
- Embedded firmware developers implementing fusion on microcontrollers.
- Robotics engineers using inertial odometry on mobile platforms.
- Flight control engineers working on drones and aircraft stabilisation.
- Calibration engineers running rate tables and climatic chambers.
- Engineering or science graduates looking to enter the aerospace sector.
What you'll work with
- IEEE Std 952-2020 (specification and test procedure for single-axis interferometric fibre optic gyros)
- IEEE Std 1293-2018 (specification and test procedure for linear single-axis, non-gyroscopic accelerometers)
- IEEE Std 1431-2004 (specification and test procedure for Coriolis vibratory gyros)
- IEEE Std 1554-2005 (recommended practice for inertial sensor test equipment, instrumentation, data acquisition and analysis)
- ISO 16063-21 (vibration calibration by comparison to a reference transducer)
- IEC 60068-2-64 (environmental testing, broadband random vibration)
- MATLAB Sensor Fusion and Tracking Toolbox
- Python with NumPy and FilterPy
- ROS 2
- STM32CubeIDE
- Allan Variance ROS
8 modules
1.1Operating principle of capacitive accelerometers1.2Coriolis vibratory gyroscopes1.3AMR, TMR and Hall-effect magnetometers1.4Internal architecture of a six- and nine-axis IMU1.5SPI and I2C interfaces and sampling synchronisation1.6Bandwidth, range and resolution1.7Comparison of MEMS, FOG and ring laser gyroscopes1.8Packaging, mounting and stress sensitivity1.9Datasheets and critical parameters1.10Practical: acquiring raw data from an IMU
2.1Bias, scale factor and misalignment2.2Bias stability and angle random walk2.3Allan variance and interpretation of slopes2.4Gyroscope acceleration sensitivity2.5Thermal drift and hysteresis2.6Quantisation noise and aliasing2.7Vibration effects and rectification2.8Specifications according to IEEE Std 952 and 12932.9Stochastic error models2.10Lab: Allan variance analysis
3.1Multi-position calibration on a positioning table3.2Calibration on a rate table3.3Six-position accelerometer calibration3.4Hard-iron and soft-iron magnetometer calibration3.5Thermal compensation using a climatic chamber3.6Estimation of misalignment matrices3.7Field calibration without reference equipment3.8Storing coefficients in firmware3.9Verification and repeatability of the calibration3.10Workshop: magnetometer calibration with ellipsoid fitting
4.1Euler angles and gimbal lock4.2Direction cosine matrices4.3Quaternions and rotation algebra4.4Integration of angular rates4.5ENU, NED and body reference frames4.6Gravity, initial alignment and levelling4.7Inertial dead-reckoning navigation4.8Error propagation in integration4.9Numerical precision on microcontrollers4.10Practical: quaternion integration in C
5.1Complementary filter and its tuning5.2Mahony and Madgwick filters5.3Linear Kalman filter applied to attitude5.4Extended Kalman filter for AHRS5.5Unscented Kalman filter (UKF)5.6Error-state Kalman filter (ESKF)5.7Tuning process and measurement covariances5.8Detection of magnetic disturbances5.9Observability and filter convergence5.10Lab: attitude EKF with real data
6.1Real-time firmware architectures6.2Fixed-point versus floating-point arithmetic6.3Sensor-integrated fusion units6.4Time synchronisation and timestamps6.5FIFO and interrupt management6.6Power consumption and low-power modes6.7Unit testing of fusion algorithms6.8Integration in ROS 26.9Diagnostics and sensor fault detection6.10Workshop: AHRS on a Cortex-M microcontroller
7.1Drone stabilisation and flight control7.2Mobile robotics and inertial odometry7.3Human motion tracking7.4Inertial navigation in vehicles7.5Integration with GNSS and visual odometry7.6Optical references for validation7.7Attitude and heading error metrics7.8Vibration and temperature testing7.9System performance report7.10Case study: validating an AHRS in a vehicle
8.1Specification of attitude requirements8.2Sensor and architecture selection8.3Characterisation with Allan variance8.4Complete calibration procedure8.5Design of the fusion filter8.6Embedded implementation and testing8.7Validation against a reference8.8Analysis of results and improvements8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
Delivered 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB Sensor Fusion and Tracking Toolbox, Python with NumPy and FilterPy, ROS 2, STM32CubeIDE and Allan Variance ROS, in free, academic or evaluation versions. Testing is studied through real procedures: multi-position calibration on a rate table, thermal stability tests in a climatic chamber and attitude validation against an optical motion-capture system. Assessment is continuous by module, plus the final integrating project.
Standards covered: IEEE Std 952-2020, IEEE Std 1293-2018, IEEE Std 1431-2004, IEEE Std 1554-2005, ISO 16063-21, IEC 60068-2-64.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with MATLAB Sensor Fusion and Tracking Toolbox, Python with NumPy and FilterPy, ROS 2, STM32CubeIDE and Allan Variance ROS, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the programme software, and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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