Adaptive and Array Signal Processing

Module Number: EI7433

Duration: 1 Semester

Ocurrence: Winter Semester

Language: English

Number of ECTS: 5


Professor in charge: Wolfgang Utschick

Amount of work

Contact hours: 60

Self-study hours: 90

Total: 150

Description of achievement and assessment methods

Knwoledge-based learning outcomes are reviewed as part of a 90 minute written examination. During the semester, there are four mandatory home-works for enhancement of MatLab programming skills. The weights of these exams are:

  • 90% Final Examination
  • 10%Homework

In addition, there is a voluntary mid-term examination which will be graded, but it will be credited only if results in an improvement of the overall grade according to the following weights:

  • 65% Final Examination
  • 25% Mid-term Examination
  • 10% Homework

Exam type: written

Exam duration: 90 minutes

Possibility of re-taking: In the next semester: Yes; At the end of the semester: No

Homework: Yes

Lecture: No

Conversation: No

Written paper: No

Recommended requirements

Pre-requisite are basic knowledge of linear algebra, linear discrete time systems and linear transforms on an undergraduate level.


  • Motivation: Application areas of adaptive filters; Adaptive Equalization; Single channel (Single-sensor) temporal processing; Multichannel (multi-sensor) spatial/spatio-temporal processing.
  • Mathematical Background: Gradients; Complex analysis; Quadratic Optimization with Linear Constraints; Method of Langrangian multipliers for complex valued problems; Stochastic processes, correlation and covariance matrices; Matrix decomposition (eigenvalue and singular value decomposition); Solving linear system of equations and least-squares problems.
  • Linear Optimum Filtering: Wiener filtering; Spatial Filtering: Minimum Variance Distortionless Response (MVDR) Beamforming and Generalized Sidelobe Canceller (GSC; Iterative solution of normal equations; Gradient descent and Least Mean Square (LMS) algorithm

Study goals

At the end of the module students are able to analyze and to design signal processing algorithms both in the temporal and spatial domain to support wireless and wired communication.

Teaching and learning methods

  • Learning method: In addition to the individual methods of the students consolidated knowledge is aspired by repeated lessons in exercises and tutorials
  • Teaching method: During the lectures students are instructed in a teacher-centered style. The exercises are held in a student-centered way.

Media formats

For teaching purposs, various media are useded such as blackboard, beamer presentation, slides, lecture script and moodle


The following literature is recommended:

  • Strang G: Linear Algebra and its ApplicationsHaykin, S.: Adaptive Filter Theory