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.
LEARNING OUTCOMES
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 (visa.koivunen@aalto.fi)
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
CONTENT, ASSESSMENT AND WORKLOAD
Content
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
- Optimal Estimation techniques: Maximum Likelihood, MVUE, performance bounds
- Statistically Robust Estimators: dealing with noise and model uncertainty
- 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
- Wiener filter, Adaptive LMS-filter
- Kalman filter
- Extended KF, Unscented KF
- Sensor Array Signal Processing:
- Beamforming and and Direction of arrival estimation
- High Resolution and Optimal Methods
- 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
Workload
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.
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
Handout
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
Prerequisites
Valid 01.08.2020-31.07.2022:
Basic knowledge of matrix algebra, probability and statistics.
SDG: Sustainable Development Goals
9 Industry, Innovation and Infrastructure
FURTHER INFORMATION
- Opettaja: Koivunen Visa
- Opettaja: Rajamäki Robin