ET4386 Estimation and Detection
Introduction
This course covers the basics of detection and estimation theory, as used in statistical signal processing, adaptive beamforming, speech enhancement, radar, telecommunication, loclization, system identification, and elsewhere.Part I: Optimal estimation covers minimum variance unbiased (MVU) estimators, the CramerRao bound (CRB), best linear unbiased estimators (BLUE), maximum likelihood estimation (MLE), recursive least squares (RLE), Bayesian estimation techniques, and the Wiener filter.
Part II: Detection theory covers simple and multiple hypothesis testing, the NeymanPearson Theorem, Bayes Risk, and testing with unknown signal and noise parameters.
The course complements EE4c03 Statistical digital signal processing and modeling, and gives a solid background for EE4715 Array Processing and EE4685 Machine learning, a Bayesian perspective.
Preliminary knowledge
To follow the course with profit, you will need the background knowledge provided by an elementary course in Random Signals.
Exam
In principle, the exam in the study year 2021/2022 will be a written exam. However, depending on the developments of the Covid19 Pandemic, the course is subject to change. In case the Covid19 pandemic makes it difficult to have a written exam, this will be changed into an oral exam.
The exam is closed book, but, students are allowed to bring a double sided self handwritten A4 formula sheet.
As part of the course, there is a compulsary mini project, which helps you to get experienced with the theory and to apply this to a practical problem. The available mini projects will be announced via the course website, after which students can sign in via Brightspace. The mini projects can be done in pairs. To sign up for a project, go to brightspace, enroll for the course, and then go to the tab "collaboration", and select "groups".
The final report on the project needs to be uploaded in pdf before January 10th 2022 via brightspace.
The project report will be graded and counts for 20 % of the final grade. Access to the final exam will only be granted if the project is handed in.
Projects
 Project 1: "MultiMicrophone Speech Enhancement". Description  Data
 Project 2: "Signal Detection". Description  Data
 Project 3: "Cognitive radio". Description  Data
 Project 4: "Clock Synchronization for wireless sensor networks". Description  Data
 Project 5: "Accelerometer Calibration". Description  Data
To sign up for the miniprojects, go to the course page on brightspace. Then go to the tab "collaboration", and then select groups. Signing up can be done until December 1st 2021. After that the enrolment for the projects will close. To upload your final assignment (before January 10th 2022), do this via "assignments" in brightspace.
For questions on the projects, please email at et4386EWI@TuDelft.nl
Books
 Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory; S.M. Kay, Prentice Hall 1993; ISBN13: 9780133457117.
 Fundamentals of Statistical Signal Processing, Volume II: Detection Theory; S.M. Kay, Prentice 1993; ISBN13: 9780135041352.
Instructors
The lectures will be given by Dr. ir. Richard C. Hendriks (RCH) and Dr. Raj Thilak Rajan (RTR).Contact
Please use the following email address for any inquiries regarding the course and the mini projects:Course material
Individual files in PDF format are available below. As the course develops additional files with e.g., solutions to the exercises, will be posted.Schedule
The schedule for 2021/2022 is as follows:
Date  Book  Slides  lecture videos  

1.  Nov. 8  RCH/RTR 
Introduction. Estimation theory  MVU, CRB  Vol.1 Chapters 1 and 2  MVU  Introduction/MVU  
2.  Nov. 11 
RTR 
Estimation theory: Cramer Rao Lower Bound (CRB)  Vol.1 Chapter 3  3.5 , Chapter 5  CRB  
3.  Nov 15  RTR  Estimation theory: Best Linear Unbiased Estimators (BLUE), Maximum likelihood estimation (MLE) 
Vol.1: Ch. 3.7, Ch. 6.1  6.5 and Ch. 7.1  7.6  BLUE/MLE


4.  Nov 18  RTR  Estimation theory  Least squares (LS)  Vol.1: Ch. 8.1  8.4 and 8.88.9  Least Squares  LS  
5.  Nov 22  RTR  Estimation theory  Bayesian philosophy  Vol.1 Ch. 1010.6  Bayesian  Bayesian philosophy  
6.  Nov 25  RTR  Estimation theory  Bayesian estimators 
Vol.1 Ch. 1111.5 vol. 1 Ch. 1212.5 
MAP  LMMSE  MAP/LMMSE  
7.  Nov 29  RTR  Wiener filters 
Vol.1 Ch. 12.7    
8.  Dec 2  RCH  Detection theory  Introduction, Neyman Pearson theorem  Vol.2 Chapters Ch. 33.7  Introduction detection  Intro Det. Theory  
9.  Dec 6  RCH  Detection theory  Deterministic signals  Vol.2 Chs. 44.4  Deterministic Signals  
10.  Dec 9  RCH  Detection theory  Random Signals  Vol.2 Chapters 55.6  Detection  Random signals  Random Signals/GLRT  
11.  Dec 13  RCH  Detection theory  GLRT  Vol.2 Chapters 66.4  GLRT  
12.  Dec 16  RCH/RTR  Exercises  
13.  Dec 20  RCH  Estimation of Sensor Array Signal Model Parameters Using Factor Analysis: Biomedical and audio array processing  
14.  Dec 23  RTR  Estimation & Detection: Sensor calibration 
Exercises
The book contains many examples and exercises. A (incomplete) list with recommanded exercises from the book can be downloaded here. In addition, some extra examples and exercises are given in the list below:
 Example MVU & CRLB
 Exercise 10.3
 Example MVU, CRLB & Bayesian Estimation.
 Bayesian Estimation (Answers)
 Detection Theory (Answers)
 Estimation Theory (Answers)
Example exams