3. 강의목표
The course objective is to let the student obtain
1) the capability of identifying the engineering problems that can be put into the frame of statistical signal processing,
2) the capability of solving the identified problems using the standard techniques learned through this course, and
3) the fundamental understanding of statistical signal processing that may help them study advanced topics and consequently make significant contributions to the theory and the practice of statistical signal processing.
4. 강의선수/수강필수사항
Undergraduate Level
Signals and Systems,
Digital Communications,
Probability, Random Variables, and Random Processes
Graduate Level (Optional):
Digital Communications,
Optimization Theory,
Computer programming skills (MATLAB or C).
Contact the instructor for questions about prerequisites.
5. 성적평가
1. 1st midterm exam (TBD, 7-10 pm, In-class, Lec. 1-8) 15 %
2. 2nd midterm exam (TBD, 7-10 pm, In-class, Lec. 1-18) 20 %
3. Final exam (TBD, In-class, Lec. 1-30) 25 %
4. Homework (Quiz) 20 %
5. Project and Participation 20 %
100 %
7. 참고문헌 및 자료
Lecture notes and videos on the course web.
Reading the following references is strongly recommended.
P: H. V. Poor, An introduction to signal detection and estimation 2nd ed, New York : Springer-Verlag, 1994.
Sc: L. L. Scharf, Statistical Signal Processing. Reading, MA: Addition-Wesley, 1991.
V: H. L. Van Trees, Detection, Estimation, and Modulation Theory, Wiley, 1971.
Sr: M.D. Srinath, R.K. Rajasekaran, and R. Viswanathan, Introduction to Statistical Signal Processing with Applications, Prentice Hall, 1996.
K1: S. M. Kay, Fundamentals of Statistical Signal Processing:Estimation Theory, Prentice Hall, 1993.
K2: S. M. Kay, Modern spectral estimation: theory and application, Prentice Hall, 1988.
G: R. M. Gray and L. D. Davisson, An Introduction to Statistical Signal Processing, Cambridge Univ. Press, 2004.
8. 강의진도계획
I. IntroductionII. Statistical Inference
Vector observation- Bayesian detection and estimation- Non-Bayesian detection and estimationSequence observation- Convergence of a random sequence- Bayesian detection and estimation- Non-Bayesian detection and estimationWaveform observation- KL expansion, Sampling theorem, etc.- Bayesian detection and estimation- Non-Bayesian detection and estimationIII. Non-statistical InferenceLeast squaresMethods of momentsSpectral estimationIV. Recent Advances in Statistical Signal Processing
9. 수업운영
본 과목은 기녹화된 강의 동영상을 수강생이 미리 공부해 오는 Flipped Learning 방식으로 진행됩니다.
10. 학습법 소개 및 기타사항
Course homepage is
http://cisl.postech.ac.kr/class/eece645/index.htm
11. 장애학생에 대한 학습지원 사항
- 수강 관련: 문자 통역(청각), 교과목 보조(발달), 노트필기(전 유형) 등
- 시험 관련: 시험시간 연장(필요시 전 유형), 시험지 확대 복사(시각) 등
- 기타 추가 요청사항 발생 시 장애학생지원센터(279-2434)로 요청