Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


To be able to explain and use basic methods of statistical signal processing and apply them to various problems in engineering, data analytics and multisensor systems.

Credits: 5

Schedule: 08.09.2020 - 10.12.2020

Teacher in charge (valid 01.08.2020-31.07.2022): Visa Koivunen, Esa Ollila, Esa Ollila

Teacher in charge (applies in this implementation): Visa Koivunen

Contact information for the course (valid 07.09.2020-21.12.2112):

Lecturer prof. Visa Koivunen (

Course TAs and tutoring: 

M.Sc Petteri Pulkkinen: parameter estimation

M.Sc Robin Rajamäki, optimum filtering and sensor array processing

If COVID-19 and the number of attendees allows, tutoring sessions will be organized in a classroom.

CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):

Teaching language: English

Languages of study attainment: English


  • Valid 01.08.2020-31.07.2022:

    Estimation, optimal and adaptive filtering, sensor array processing and multisensor systems

  • Applies in this implementation:

    Outline of the course

    This course covers the following topics: 

    • Basic Concepts of Parameter Estimation
    • Estimation of Deterministic Parameters
    1. Optimal Estimation techniques: Maximum Likelihood, MVUE, performance bounds
    2. Statistically Robust Estimators: dealing with noise and model uncertainty
    3. Practical but not necessarily optimal methods: Least Square, Method of Moments
    • Estimation of Random Parameters: Bayesian approach, MMSE and MAP
    • Optimum filtering and Signal estimation
    1. Wiener filter, Adaptive LMS-filter
    2. Kalman filter
    3. Extended KF, Unscented KF
    • Sensor Array Signal Processing:
    1. Beamforming and and Direction of arrival estimation
    2. High Resolution and Optimal Methods
    3. Dealing with coherent signals

Assessment Methods and Criteria
  • Valid 01.08.2020-31.07.2022:

    Lectures, exercises and an exam. Taking the exam may require passed exercises.

  • Applies in this implementation:

    • 2 sets of take-home assignments
    • Final exam

    Final grade determined 50% from the take-home assignments and 50% from final exam 

  • Valid 01.08.2020-31.07.2022:

    5 cr = 133 h

    Lectures, Exercises, Exam approximately 30 h, Independent studying (homeworks, preparing for exam, etc.) approximately 103 h

    Attendance in some contact teaching may be compulsory.


Study Material
  • Valid 01.08.2020-31.07.2022:


  • Applies in this implementation:

    Lecture notes in pdf-format will be made available as we go.

    Additional reading material will be provided.

Substitutes for Courses
  • Valid 01.08.2020-31.07.2022:

    Replaces course S-88.4200 Statistical Signal Processing P

  • Valid 01.08.2020-31.07.2022:

    Basic knowledge of matrix algebra, probability and statistics.

SDG: Sustainable Development Goals

    9 Industry, Innovation and Infrastructure



Registration and further information