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Algorithm Design for Phased Array Radar

Offered in 112-2
  • Notes
  • NTU Enrollment Status

    Enrolled
    0/30
    Other Depts
    0/0
    Remaining
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    Registered
    0
  • Course Description
    1. 本學期所有課程將會「實體聚集並支援線上會議室同步參與」,連結為 https://ntucc.webex.com/meet/yenminghuang 2. 歡迎所有對相控陣列雷達系統之實務設計和開發有興趣的同學,自在地加入我們的課程社團一起討論和分享,連結為 https://www.facebook.com/groups/ntuphasedarrayradar/ 3. 課程投影片將公告並隨時更新於 https://sites.google.com/view/yenming/teaching 4. 歡迎訂閱課程影片YouTube頻道: https://www.youtube.com/@ntuphasedarrayradar 5. 由於本課程的時段和形式較為特殊,若有任何問題,請隨時與授課教師聯絡 yenminghuang@ntu.edu.tw ------------------------------------------------------------------------- Algorithm Design for Phased Array Radar is a graduate-level course designed for students interested in modern radar systems widely used in vehicle networks, surveillance systems, military applications, satellites, etc. This course is the extension of the course entitled Signal Processing for Phased Array Radar to provide more insights into high-tech radar systems and applications in recent years. From the aspects of digital signal and data processing algorithms with the aid of Artificial Intelligence (AI), we explore advanced radar technologies.
  • Course Objective
    The goal of this course is to introduce essential digital signal and data processing techniques for phased array radar systems. By taking this course, the students can - understand the basic principles of radar, - comprehend the commonly used signal and data processing algorithms at radar receivers, and - explore advanced research topics in future radar transceivers. In addition, by studying some selected topics and executing a term project in one semester, like a workshop, the students can - be familiar with radar technology and its AI-based data usage, - share their own opinions through oral presentations in classes, and - actualize the interested algorithms by teamwork.
  • Course Requirement
    Prerequisite: - Linear Algebra - Signal and System - Principle of Communications Preferable: - Signal Processing for Phased Array Radar - Digital Signal Processing - Detection and Estimation - Adaptive Signal Processing Skill: - MATLAB (other programming languages are also okay) - Markdown and LaTeX Study on Selected Topics, Papers, or Book Chapters: - Figuring out the system model and revealing the key proposed concepts - Algorithm implementation and reconstruction of the simulation results
  • Expected weekly study hours after class
  • Office Hour

    Appointment by email.

    *This office hour requires an appointment
  • Designated Reading
  • References
    Primary Textbooks: - M. A. Richards, Fundamentals of Radar Signal Processing, 2nd edition, McGraw-Hill Education, 2014. - M. A. Richards, J. A. Scheer, and W. A. Holm, Principles of Modern Radar: Basic Principles, SciTech Publishing, 2010. - W. L. Melvin and J. A. Scheer, Principles of Modern Radar: Advanced Techniques, SciTech Publishing, 2012. - W. L. Melvin and J. A. Scheer, Principles of Modern Radar: Radar Applications, SciTech Publishing, 2013. - T. W. Jeffrey, Phased-Array Radar Design: Application of Radar Fundamentals, SciTech Publishing, 2009. - H. L. Van Trees, Optimum Array Processing: Part IV of Detection, Estimation, and Modulation Theory, John Wiley & Sons, Inc., 2002. Auxiliary Textbooks: - R. J. Mailloux, Phased Array Antenna Handbook, 3rd ed., Artech, 2017. - J. Guerci, Space-Time Adaptive Processing for Radar, 2nd ed., Artech, 2014. - V. C. Chen, The Micro-Doppler Effect in Radar, 2nd ed., Artech, 2019. - F. Fioranelli, H. Griffiths, M. Ritchie, and A. Balleri Micro-Doppler Radar and its Applications, SciTech Publishing, 2020. - J. Li and P. Stoica, MIMO Radar Signal Processing, Wiley-IEEE Press, 2009. - W. Liu and S. Weiss, Wideband Beamforming: Concepts and Techniques, John Wiley & Sons, Inc., 2010. - K. F. Warnick, R. Maaskant, M. V. Ivashina, D. B. Davidson, and B. D. Jeffs, Phased Array for Radio Astronomy, Remote Sensing, and Satellite Communications, Cambridge University Press, 2018. - S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice-Hall PTR, 1993. - S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory, Prentice-Hall PTR, 1998. - S. M. Kay, Fundamentals of Statistical Signal Processing: Practical Algorithm Development, Prentice-Hall PTR, 2013.
  • Grading
    30%

    Participation

    Please see Lecture 0: Course Information and Overview for details in the website https://drive.google.com/file/d/19AIViIJpilTFPtjPJcrNwq_Q1YOwdp3L/view

    30%

    Presentation of Selected Topics

    Please see Lecture 0: Course Information and Overview for details in the website https://drive.google.com/file/d/19AIViIJpilTFPtjPJcrNwq_Q1YOwdp3L/view

    10%

    Term Project Poster and Report

    Please see Lecture 0: Course Information and Overview for details in the website https://drive.google.com/file/d/19AIViIJpilTFPtjPJcrNwq_Q1YOwdp3L/view

    30%

    Term Project

    Please see Lecture 0: Course Information and Overview for details in the website https://drive.google.com/file/d/19AIViIJpilTFPtjPJcrNwq_Q1YOwdp3L/view

  • Adjustment methods for students
    Adjustment MethodDescription
    Teaching methods

    Assisted by video

    Provide students with flexible ways of attending courses

    Assignment submission methods

    Mutual agreement to present in other ways between students and instructors

    Others

    Negotiated by both teachers and students

  • Course Schedule
    20240224Week 1Lecture 0: Course Information and Overview
    20240302Week 2Lecture 1: Constant False Alarm Rate Detection
    20240309Week 3Lecture 1: Constant False Alarm Rate Detection
    20240316Week 4Lecture 2: Matched Filtering and Pulse Waveform
    20240323Week 5Lecture 2: Matched Filtering and Pulse Waveform
    20240330Week 6Lecture 2: Matched Filtering and Pulse Waveform
    20240406Week 7No class.
    20240413Week 8Lecture 3: Doppler Phenomenology and Processing
    20240420Week 9Lecture 3: Doppler Phenomenology and Processing
    20240427Week 10Lecture 4: Array Characterization and Processing
    20240504Week 11Lecture 5: Target Tracking with Data Association
    20240511Week 12No class.
    20240518Week 13Term Project: Market Application and Product
    20240525Week 14Term Project: Literature Survey and Review
    20240601Week 15Term Project: Classic Algorithm Introduction
    20240608Week 16No class.