Please note! Course description is confirmed for two academic years, 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: 14.09.2021 - 16.12.2021

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Visa Koivunen, Robin Rajamäki

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English


  • valid for whole curriculum period:

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

  • applies in this implementation

    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 for whole curriculum period:

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

  • applies in this implementation

    Two take-home assignment sets containing both analytical problem solving and matlab-assignments that have to be solved independently.

    Final exam

    Grading is based on the take-home assignments (50%) and final exam (50%).

  • valid for whole curriculum period:

    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 for whole curriculum period:


  • applies in this implementation

    Handouts will be provided as we progress with the lectures.

    Additional reading material will be provided as well as some tutorial material that helps solving the assignments.

Substitutes for Courses
SDG: Sustainable Development Goals

    9 Industry, Innovation and Infrastructure


Further Information
  • valid for whole curriculum period:

    Teaching Period:

    2020-2021 Autumn I-II

    2021-2022 Autumn I-II

    Course Homepage:

    Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu ( instead of WebOodi.

    In WebOodi

Details on the schedule
  • applies in this implementation

    The course will move to in-person teaching as soon as Aalto COVID-19 regulations allow.