In modern sensor systems, estimation and sensor fusion play a significant part in the design of the multiple sensors. 

Therefore, this course focuses on fundamental understanding, demonstration, and applications of basic and advanced estimation theories, multiple sensor fusion techniques, and their architectures, algorithms, and applications. This should enable you to critically select and design appropriate estimation and multiple sensor fusion techniques to your specific problems depending on the types of sensor systems and noise characteristics of sensors.

At a glance

  • Dates
    • 25 Nov - 04 Dec 2024
  • Duration8 days (half day sessions)
  • LocationCranfield campus
  • Cost£1,800 Concessions available

Course structure

This course consists of lectures, case studies, and lab sessions on estimation and fusion algorithms. Matlab will be used during lab sessions. All delegates will receive a Certificate of Attendance at the end of the course.

What you will learn

The aim of this course is to acquaint you with the basic principles of estimation theory, and critically understand the pros and cons of filtering and fusion theories when applied to the problem of sensor fusion.

On successful completion, you will be able to:

  • Demonstrate the nature, purpose, and design procedures of estimation theory and sensor fusion
  • Critically understand challenging problems in the conventional estimation and sensor fusion approaches
  • Critically select and apply an appropriate filtering technique and sensor fusion method to a specific problem depending on the types of system/sensor dynamics and noise characteristics.

Core content

Topics covered by the course include:

  • Introduction on estimation theories and sensor fusion
  • Statistical analysis: Gaussian distributions, expectation operator, means and variances, maximum likelihood
  • Observers: Principle of internal model matching, using outputs to match internal model, full state observer, reduced state observer
  • Estimators: Linear Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, Adaptive Filter (IMM Filter), Information Filter, Particle Filter
  • Sensor integration architectures: central, hierarchical, and decentralised fusion architectures
  • Multiple sensor fusion: Covariance intersection, State-vector fusion (track-to-track fusion), Information fusion.

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Who should attend

This course is ideal for engineers with interest in estimation theories, sensor fusion and their architecture, algorithms and applications.


  • Dr Hyo-Sang Shin
  • Prof Antonios Tsourdos
  • Prof Rafal Zbikowski


20% discount for Cranfield Alumni. 
10% discount when registering 3 or more delegates, from the same organisation at the same time.  

Accommodation fees are not included in the discount scheme. Please ask about our discount scheme at time of booking.

Accommodation options and prices

This course is non-residential. If you would like to book accommodation on campus, please contact Mitchell Hall or Cranfield Management Development Centre directly. Further information about our on campus accommodation can be found here.  Alternatively you may wish to make your own arrangements at a nearby hotel. 

Location and travel

Cranfield University is situated in Bedfordshire close to the border with Buckinghamshire. The University is located almost midway between the towns of Bedford and Milton Keynes and is conveniently situated between junctions 13 and 14 of the M1.

London Luton, Stansted and Heathrow airports are 30, 90 and 90 minutes respectively by car, offering superb connections to and from just about anywhere in the world. 

For further location and travel details

Location address

Cranfield University
College Road
MK43 0AL

How to apply

To apply for this course please use the online application form.

Read our Professional development (CPD) booking conditions.