Program Overview
Cranfield University's "Advanced Sensor Data Processing" course provides engineers and managers with in-depth knowledge of modern sensor systems and processing techniques. Through hands-on MATLAB implementation, participants gain expertise in adaptive signal processing, sonar signal processing, Bayesian belief networks, multi-layer perceptrons, and fuzzy logic. The course is designed for military officers and defense industry professionals, and can be taken for credit towards the Military Electronic Systems Engineering MSc. program.
Program Outline
Degree Overview:
Advanced Sensor Data Processing
is a short course offered by Cranfield University, aimed at providing participants with an in-depth understanding of processing algorithms and methods applicable to modern sensor systems.
Objectives:
- Equip engineers and managers with knowledge of modern sensor systems and their processing techniques.
- Provide a comprehensive understanding of various signal processing algorithms and their applications.
- Enhance participants' ability to analyze, design, and develop sensor systems for various applications.
Description:
This intensive 5-day course delves deep into the intricacies of modern sensor systems, covering a wide range of topics including adaptive signal processing, sonar signal processing, Bayesian belief networks, multi-layer perceptrons, and fuzzy logic. The course utilizes the powerful software tool MATLAB to implement and analyze these techniques. Participants will gain hands-on experience in applying these processing algorithms to real-world sensor systems, enhancing their ability to solve complex problems in diverse fields.
Outline:
- Structure: The course runs once a year and spans 5 consecutive days.
- Content: The curriculum covers the following key areas:
- Adaptive Signal Processing: Explores adaptive FIR and spatial filtering, error surface analysis, optimization algorithms like LMS, and practical applications.
- Sonar Signal Processing: Examines beamforming techniques for both passive and active processing applications.
- Bayesian Belief Networks: Introduces Bayesian inference as a tool for handling uncertainties in sensor data.
- Multi-layer Perceptrons: Delves into neural network architecture, learning algorithms like backpropagation, and applications in unsupervised learning, Hebbian learning, Kohonen maps, and network design using MATLAB.
- Fuzzy Logic: Covers the fundamentals of fuzzy logic, including fuzzy associative matrices and fuzzy inference.
- Adaptive Linear Elements: Investigates tapped delay lines, noise cancellation techniques, time-series prediction, Elman networks, evolutionary algorithms, and their implementation in MATLAB.
- Modules: Each topic listed above constitutes a separate module within the course, providing a comprehensive and in-depth understanding of each processing technique.
Assessment:
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Teaching:
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Careers:
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Other:
- Participants can choose to take this course for credit towards the Military Electronic Systems Engineering MSc. program.
- Concessions on fees are available for eligible participants.
- The course is specifically designed for military officers and defense industry professionals.
- The course is delivered at the Cranfield Defence and Security campus, located near Swindon, UK.
