UNIK9361 - Radar - systems and signal processing
The course gives an introduction to how a radar functions, and especially to the digital signal processing used in modern systems. The most important processes for the radar’s performance will be covered, including propagation and reflection of electromagnetic radiation, the radar equation, waveforms, array antennas, Doppler processing, detection theory and tracking. Through exercises using modern numerical tools you will gain practical experience with numerical methods for digital signal processing and calculation of radar performance.
As a PhD candidate you will focus on more advanced topics within the radar field, and gain in-depth knowledge in at least one area trough research work on your doctoral dissertation (possible topics include passive radar, techniques for adaptive beamforming and advanced waveform).
After completing the course you will:
- Know how a radar is built and understand the principles of behavior.
- Have a basic understanding of how radar signals propagate through a medium, and the mechanisms for signal reflection from the target and unwanted reflections (“clutter”).
- Understand the basic principles of signal processing done in a radar.
- Be able to estimate the performance of a radar based on parameters provided, for example at what distance the radar will be able to detect targets of a given size.
- Be able to assess what type of radar is suitable for which task (choice of waveforms, frequency bands, etc..).
- Be able to use numerical tools to calculate radar performance and to simulate the signal processing in a radar.
- Have a thorough understanding of at least one area in the radar field and be and be able to pass this on to other with a basic understanding of radar.
PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.
If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.
PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.
Recommended previous knowledge
The course is based on basic mathematics, including calculations with complex numbers, basic Fourier analysis and probability theory. The exercises are based on modern numerical tools (python, matlab, octave, idl or similar), and students with no experience with at least one such tool must expect extra effort here.
Individual courses that provide useful background knowledge, but that is not necessary in order to benefit from the course, is MAT1100 - Calculus, MAT1110 - Calculus and linear algebra, MAT-INF1100 - Modelling and computations, INF1100 - Introduction to programming with scientific applications (continued) og FYS1120 - Electromagnetism.
3 hours teaching each week (2 hours of lectures and 1 hour exercises). The distribution of lectures vs exercises may vary throughout the semester.
Two mandatory exercises (one for all students, and one directed towards each candidate's ph.d. focus) must be submitted and approved in order for the students to attend the exam.
A final, oral exam at the end of the semester will count for 100% of the final grade (in case of many students, there may be held a written exam).
Two mandatory exercises must be submitted and approved in order for the students to attend the exam.
Grades are awarded on a pass/fail scale. Read more about the grading system.
Explanations and appeals
Resit an examination
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.