Advanced Topics on Signal Processing
Keywords |
Classification |
Keyword |
OFICIAL |
Telecommunications Engineering |
Instance: 2024/2025 - 1S 
Cycles of Study/Courses
Acronym |
No. of Students |
Study Plan |
Curricular Years |
Credits UCN |
Credits ECTS |
Contact hours |
Total Time |
MAP-T |
2 |
Syllabus since 2022/23 |
1 |
- |
6 |
56 |
162 |
Teaching Staff - Responsibilities
Teaching language
Portuguese and english
Objectives
This course aims to motivate students to fundamental and advanced signal processing concepts and techniques in the areas of spectrum estimation, adaptive filtering, sonar, radar, and synthetic aperture systems.
Learning outcomes and competences
The objective is to empower students with analysis and design skills in a variety of technologies and application areas including robust signal modeling, estimation, detection, and communication, as well as robust monitoring and detection of signals, objects, and environmental conditions.
Working method
Presencial
Pre-requirements (prior knowledge) and co-requirements (common knowledge)
Basic knowledge of signal processing concepts and techniques.
Program
1. Background Review
a. The Z-Transform, the DFT and the FFT
b. The DFT as a uniform filter bank
c. Discrete-time random processes
2. Spectrum Estimation
a. Non-parametric methods
b. Estimation of the frequency, magnitude and phase of sinusoids
c. Frequency estimation using eigen-analysis
d. Principal components spectrum estimation
3. Adaptive filtering
a. Basic concepts and configurations
b. The MSE algorithm and other variants
c. Filter bank-based adaptive filtering
d. Applications of adaptive filtering
4. The matched filter
a. Paradigm
b. Processing gain
c. Relevance in communications
d. OFDM and the matched filter
5. Signal processing in sonar/radar
a. Principles of radar/sonar
b. Relationship with the matched filter theory
c. Signals in radar/sonar: requirements and examples
d. Design of a simple sonar
6. Signal processing in synthetic aperture systems
a. Synthetic aperture radar/sonar
b. Relationship with the matched filter
c. Usage of the FFT for efficiency
d. Application to real synthetic aperture data
Mandatory literature
Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon; Statistical and adaptive signal processing, Artech House, 2005. ISBN: 1580533663
Mark Richards, James Scheer, William Holm; Principles of Modern Radar – Vol. I: Basic Principles, SciTech Publishing, 2010. ISBN: 978-1-891121-52-4
Simon Haykin;
Adaptive filter theory. ISBN: 0-13-322760-X
Teaching methods and learning activities
The topics of the curricular unit will be explained and discussed in lectures (one 3-hour class per week) where applied exercises and case studies illustrating the techniques and technologies in focus and based on signal processing will be addressed. These exercises and case studies will also be the object of specific home assignments that will contribute to assessment. Relevant teaching materials will be made available to students on the Moodle platform.
Software
Matlab
Evaluation Type
Distributed evaluation without final exam
Assessment Components
Designation |
Weight (%) |
Participação presencial |
10,00 |
Teste |
30,00 |
Trabalho prático ou de projeto |
60,00 |
Total: |
100,00 |
Amount of time allocated to each course unit
Designation |
Time (hours) |
Estudo autónomo |
70,00 |
Frequência das aulas |
56,00 |
Trabalho de campo |
16,00 |
Trabalho escrito |
20,00 |
Total: |
162,00 |
Eligibility for exams
Attendance in the curricular unit is recognized for students who do not exceed the limit of absences (established in the FEUP General Evaluation Rules) and who have participated in at least 70% of the assessment moments/elements specified throughout the semester.Calculation formula of final grade
The final grade will be obtained by averaging the grades obtained in mini-tests, specific homework assignments, and/or mini-projects to be developed in groups of students throughout the semester.