Syllabus - (8 Unknown noise parameters Homework/Projects...

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ECE 254: Detection Theory Instructor: Bill Hodgkiss Tel: 858-534-1798 FAX: 858-822-0665 E-Mail: [email protected] Campus Mail: 0701 Course Description: Hypothesis testing, detection of signals in white and colored Gaussian noise; estimation of signal parameters; maximum-likelihood detection; resolution of signals; detection and estimation of stochastic signals; applications to radar, sonar, and communications. Summary of Topics Discussed: (1) Introduction – SKE, SKEP, SKEPII problems. (2) Summary of various PDFs – Gaussian and Chi-squared. (3) Statistical decision theory framework – NP and Bayesian approaches. (4) Deterministic signals – Matched filter and generalized matched filter. (5) Random signals – Gaussian signal in Gaussian noise, Rayleigh fading signal. (6) Composite hypothesis testing (uncertain parameters) – Bayesian and GLRT detection receivers. (7) Deterministic signals with unknown parameters – amplitude, phase, frequency, and arrival time.
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Unformatted text preview: (8) Unknown noise parameters. Homework/Projects: Approximately one computer-oriented homework assignment will be made per week. These can be worked on in groups and should be turned in as soon as possible for feedback. A mid-term and an end-term project will be assigned. These should represent individual effort (i.e. should be considered as take-home exams) and assistance should not be given nor received from anyone other than the instructor. Grades: No exams will be given. Grades will be assigned based on the weekly homework assignments and the mid/end-term projects. The homework assignments count 1/3 and the mid/end-term projects count 1/3 each. Text: S. Kay. Fundamentals of Statistical Signal Processing - Vol II. Detection Theory. Prentice-Hall, 1998. ISBN 0-13-504135-X....
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