STUDENTS ARE EXPECTED TO HAVE COMPLETED: Introduction to Statistical Signal Processing (or similar)

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 advanced techniques of: - Random signals analysis - Estimation - Detection

DESCRIPTION OF CONTENTS: PROGRAMME · Parameter estimation - Method of moments - Maximum likelihood - Bayesian estimation · Signal Estimation - MMSE estimation - Linear estimation and prediction - Optimal and adaptive filtering · Hypothesis testing - Wald tests - Likelihood ratio methods - Bayesian tests

LEARNING ACTIVITIES AF3 Theoretical practical classes AF4 Laboratory practices AF5 Tutorials AF6 Team work AF7 Student individual work AF8 Partial and final exams METHODOLOGY MD1: Class lectures by the professor with the support of computer and audiovisual media, in which the main concepts of the course are developed and complemented with bibliography. MD2: Critical reading of texts recommended by the professor of the course. MD3: Resolution of practical cases, problems, etc. .... posed by the teacher individually or in groups. MD4: Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the course, as well as case studies. MD5: Elaboration of works and reports individually or in groups. CONSULTATION HOURS The students will be able to consult with the instructor during 2 or 3 hours per week