Course syllabus for Electromagnetic sensor systems

Basic knowledge in science at the graduate level.

In recent years, the demand for electromagnetic sensors has increased dramatically, from applications in cars (e.g. collision sensors) to advanced satellite instruments that monitor changes in the environment, weather and climate. This development will continue, with the demand for both smaller and cheaper electromagnetic sensors, which can be mass produced, as well as advanced satellite sensors that measure accurately with high spatial resolution. These technologies create large amounts of complex data and it is very challenging to convert them into useful and reliable data. This requires good physical understanding as well as knowledge in signal processing and data management and here machine learning very useful.

The aim of the course is to provide an overall understanding of the above parts, with special emphasis on the physical and instrumental principles that underlie the measurements.

A basic understanding of the possibilities and limitations of different electromagnetic sensor types and knowledge of the main applications is also provided. This knowledge will enable work on the design of sensor systems and provide a basis for further studies in the subject.

• Distinguish and explain the most important physical processes that affect measurements in different wavelength ranges. This includes performing calculations for simpler model systems.
• Get an overview and insight into different types of electromagnetic sensor systems and the platforms from which they can be used. 
• Describe data evaluation, from measurement to geophysical product.
• Provide examples of useful external data.
• Demonstrate an understanding of how data can be extracted from observations.
• Discuss simple quantities that describe the measurements, such as coverage, resolution in time and space, and predominant random and systematic errors.

The course describes electromagnetic sensor systems that work in different wavelength ranges and their possibilities and limitations. This is done with a focus on physical principles, hardware, data evaluation and applications. In the course, guest lecturers from industry will participate as well as a number of relevant laboratory steps where different data is extracted from laboratory measurements or existing satellite data.
The course is structured in the following sub-areas:
Physical principles: absorption, emission, scattering, reflection, radiation transport.
Hardware: spectrometers, radiometers, optics, waveguides
Measurement methods: Radar, Lidar, SAR, DOAS, FTIR, passive and active techniques.
Data evaluation: Bayesian theory and machine learning.
Platforms: satellites, aircraft, drones, autonomous vehicles.
Applications: imaging, measurement of geophysical and other parameters (gas concentration, sea ice, particle concentration, forest biomass, temperature, water vapor, wind), performance of autonomous vehicles, distance measurement (topography / bathymetry), weather forecasts.

The course includes lectures, problem solving classes and laboratory work.
Several guest lectures about sensors and machine learning will be held.

Literature information will be provided at later stage, it will include a course book, handouts and compendia that are provided free of charge.

Written exam (6 credit points ) and compulsory laboratory exercises (1.5 credit points). The final grade is solely based on exam results.