Checking date: 18/05/2022


Course: 2022/2023

Statistical Signal Processing
(16496)
Study: Bachelor in Data Science and Engineering (350)


Coordinating teacher: RAMIREZ GARCIA, DAVID

Department assigned to the subject: Signal and Communications Theory Department

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Probability and data analysis Introduction to statistical modeling Signals and systems Linear algebra
Objectives
The main objective of this course is that the student is able to extract relevant information contained in the signals under study with the aid of statistical signal processing tools. To achieve this, the student will study the main techniques of: - Random signals analysis - Estimation - Detection
Skills and learning outcomes
Description of contents: programme
This course introduces the fundamental tools for the estimation, detection, and prediction of discrete-time random signals INTRODUCTION and FOUNDATIONS: · Detection and estimation · Calculus, probability, and linear systems PART 1: Stochastic processes · Introduction and examples · First and second order statistics · Stationarity and ergodicity · Power spectral density PART 2: Estimation theory · Parameter estimation · Bayesian estimation · Time series · Filtering, prediction and smoothing · Power spectral density estimation PART 3: Detection theory · Introduction and examples · Performance metrics for detectors · Detector design · Sequential detection
Learning activities and methodology
AF1: THEORETICAL-PRACTICAL CLASSES. They will present the knowledge that students should acquire. They will receive the class notes and will have basic texts of reference to facilitate the follow-up of the classes and the development of the subsequent work. Exercises, practical problems on the part of the student will be solved and workshops and evaluation test will be held to acquire the necessary skills. AF2: Updated to allegation AF3: INDIVIDUAL OR GROUP WORK OF THE STUDENT. AF9: FINAL EXAM. In which the knowledge, skills and abilities acquired throughout the course will be assessed globally. MD1: CLASS THEORY. Exhibitions in the teacher's class with support of computer and audiovisual media, in which the main concepts of the subject are developed and the materials and bibliography are provided to complement the students' learning. MD2: LABS. Resolution of practical cases, problems, etc. raised by the teacher individually or in groups. MD3: STUDENT CONSULTATION. Individualized assistance or in group to students by the teacher.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Calendar of Continuous assessment
Basic Bibliography
  • H. L. Van Trees. Detection, Estimation and Modulation Theory (vol. 1). Wiley. 1968
  • Louis L. Scharf . Statistical signal processing. Addison-Wesley.
  • R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification. Wiley . 2001
  • S. Haykin. Adaptive Filter Theory. Prentice-Hall. 2002
  • Steven M. Kay. Fundamentals of Statistical Signal Processing (vols. 1 and 2). Prentice Hall.

The course syllabus may change due academic events or other reasons.