Adaptive Filter Theory Simon Haykin

[Helen Drewski]

Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fifth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.

Adaptive Filter Theory examines the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fifth edition, the book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.

This course explores the use of adaptive filtering algorithms and structures to learn the optimal filter or estimator and track timevarying system dynamics in order to improve the performance over static, fixed filtering techniques. Adaptive systems are implemented as part of the coursework with application to digital communications, beamforming, control systems, and interference cancellation. The final project involves creating an adaptive equalizer for digital communications over a timevarying channel.

The course materials are divided into modules which can be accessed by clicking Course Modules on the left menu. A module willhave several sections including the lectures, readings, discussions, and assignments. You are encouraged to complete the reading assignment prior to starting the lectures. Most modules run for a period of seven (7) days, exceptions are noted on the Course Outline page. You should regularly check the Calendar and Announcements for assignment due dates.

• Calculate the optimal linear filter that minimizes the Mean Squared Error (MSE) between the estimated signal (filter output) and the desired signal in wide sense stationary scenarios.
• Design adaptive algorithms that enable an iterative estimation and tracking of the optimal solution in time-varying scenarios.
• Design adaptive structures and apply to various problem domains such as adaptive equalization, interference cancellation, linear prediction, and beam forming.
• Apply adaptive filtering techniques to simulated signals in an unknown environment in order to recover the corrupted signal-of-interest.

MATLABYou will need access to a recent version of MATLAB with the Signal Processing Toolkit. The MATLAB Total Academic Headcount (TAH) license is now in effect. This license is provided at no cost to you. Send an email to soft...@jhu.edu to request your licensefile/code. Please indicate that you need a standalone file/code. You will need to provide your first and last name, as well as your Hopkins email address. You will receive an email from Mathworks with instructions to create a Mathworks account. The MATLAB software will be available for download from the Mathworks site.

You are responsible for carefully reading all assigned material and being prepared for discussion. The majority of readings are from the course text. Additional reading may be assigned to supplement text readings.

Post your initial response to the discussion questions by the evening of day 3 for that module week. Posting a response to the discussion question is part one of your grade for module discussions (i.e., Timeliness).

Part two of your grade for module discussion is your interaction (i.e., responding to classmate postings with thoughtful responses and Critical Thinking). Just posting your response to a discussion question is not sufficient; we want you to interact with your classmates. Be detailed in your postings and in your responses to your classmates' postings. Feel free to agree or disagree with your classmates. Please ensure that your postings are civil and constructive.

I will monitor module discussions and will respond to some of the discussions as discussions are posted. In some instances, I will summarize the overall discussions and post the summary for the module.

Weekly homework includes technical problems from the text book as well as MATLAB computer assignments. Technicalproblems provide opportunities to demonstrate the theoretical and mathematical aspects of each topic. MATLAB computer assignments measure the ability to apply the theory in practical scenarios to improve the estimation performance.

The course project will be assigned midway through the course. Students are given a signal corrupted by an unknown environment and must apply multiple adaptive filtering techniques to try and recover the corrupted signal-of-interest(SOI). Project includes MATLAB algorithm development and performance testing, as well as a final report that describes the problem, proposed solution, theoretical background, simulation results, summary and lessons learned.

The midterm exam will be available in Module 7 and the final exam will be available in the next-to- last Module. You will have one week to complete the exams and they will be due by 5PM exactly one week from their release. You may use the course text to complete the exams. To receive partial credit, you must show your work.

Assignments are due according to the dates posted in your Canvas course site. You may check these due dates in the Course Calendar or the Assignments in the corresponding modules. I will post grades one week after assignment due dates.

We generally do not directly grade spelling and grammar. However, egregious violations of the rules of the English language will be noted without comment. Consistently poor performance in either spelling or grammar is taken as an indication of poor written communication ability that may detract from your grade.

Deadlines for Adding, Dropping and Withdrawing from Courses
Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar ( -services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy
All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

Students with Disabilities - Accommodations and Accessibility
Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

Student Conduct Code
The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically.

Classroom Climate
JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity.

If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

The primary aim of E9 211 is to develop a mathematical theory of linear adaptive filters. Adaptation is accomplished by adjusting the free parameters of a filter according to the input data to achieve the desired output. Such adaptive algorithms are frequently encountered in many signal processing and machine learning algorithms. The adaptive signal processing course provides a comprehensive treatment of mathematical signal processing algorithms for designing optimum and linear filters; designing, implementing, and analyzing adaptive filters applied to system identification, inverse modeling (deconvolution), adaptive control, and interference cancellation; and some selected emerging topics in signal processing.

Review of linear algebra and random processes. Optimal estimation. Linear estimation. Steepest-descent algorithms. Stochastic-gradient algorithms. Least squares and recursive least squares. Kalman filtering. Particle filtering. Blind deconvolution and beamforming. Subspace tracking. Robust adaptive filters. Iterative solvers of large-scale linear systems. Selected emerging topics.

This project consists of two parts on implementing and studying adaptive filters for adaptive noise cancellation: (a) using single-channel microphone recordings and (b) using multi-channel microphone recordings.

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