ECTS Credits |
5 |

Language of instruction |
Finnish, Course can be passed in English. |

Timing |
Spring, periods 4. |

Learning outcomes |
1. is able to utilize the generic linear model as a representation for parameter estimation
2. can apply typical deterministic and random parameter estimation methods for different estimation problems
3. is able to determine statistical properties of estimators and make comparisons between them
4. can form a basic state-variable model and utilize Kalman filtering for state estimation
5. is able to apply basic methods of detection theory for solving simple detection problems
6. can implement the learned methods and assess their statistical properties with the Matlab software |

Contents |
This course provides basic knowledge of statistical signal processing, in particular, estimation theory and its applications in signal processing. Topics: 1. Introduction, 2. Modeling of estimation problems, 3. Least Squares estimation, 4. BLUE-estimation, 5. Signal detection, 6. ML estimation, 7. MS estimation, 8. MAP estimation, 9. Kalman Filter. |

Mode of delivery |
Face-to-face teaching and homework assignments. |

Learning activities and teaching methods |
Lectures (24 h), exercises (24 h) and Matlab homework assignments (20 h). |

Target group |
Computer Science and Engineering students and other Students of the University of Oulu. |

Prerequisites and co-requisites |
031078P Matrix Algebra, 031021P Probability and Mathematical Statistics |

Recommended optional programme components |
521337A Digital Filters, 031050A Signal Analysis. These courses provide complementary information on digital signal processing and stochastic signals. The courses are recommended to be studied either in advance or simultaneously. |

Recommended or required reading |
J. Mendel: Lectures in estimation theory for signal processing, communications and control, Prentice-Hall, 1995. M.D. Srinath, P.K. Rajasekaran, R. Viswanathan: Introduction to Statistical Signal Processing with Applications, Prentice-Hall, 1996, Chapter 3. Lecture notes and exercise material. |

Assessment methods and criteria |
The course is passed with intermediate exams or final exam and accepted Matlab exercise.
Read more about assessment criteria at the University of Oulu webpage. |

Grading |
The course unit utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail. |

Person responsible |
Janne Heikkilä |

Working life cooperation |
No. |