Introduction to detection and estimation theory, with applications to communicat ion, control, and radar systems; decision-theory concepts and optimum-receiver p rinciples;
detection of random signals in noise, coherent and noncoherent detect ion; and parameter estimation, linear and nonlinear estimation, and filtering.

• Introduction
• Basic concepts of statistical decision theory: Main ingredients; concepts of optimality (Bayesian and minimax approaches)
• Binary hypothesis testing: Bayesian decision rules; minimax decis ion rules; Neyman-Pearson decision rules (the radar problem); composite hypothes is testing
• Signal detection in discrete time: models and detector structures ; performance evaluation; sequential detection
• Parameter estimation: Bayesian estimation; nonrandom parameter estimati on; maximum likelihood estimation
• Signal estimation in discrete time: Kalman filter; (time permitting) Wi ener filter
• Selected applications: likelihood ratio for signaling in additive white Gaussian noise and its applications to coherent and noncoherent receiver design; multiuser detection
• Selected communications and radar applications: coherent and nonc oherent communications in additive white Gaussian noise; fading and diversity; e stimation of signal parameters
• Signal estimation in continuous time: Kalman filter
• Signal detection in continuous time (basic theory): Radon-Nikodym deriv atives; Grenander's theorem; Karhunen-Loeve expansions and Mercer's theorem; Pitcher's theorem, detection of nonrandom and random signals in Gaussian noise

H.V. Poor, An Introduction to Signal Detection and Estimation, Springer-Verlag, 1988.

ECE 434 or equivalent.

1 unit.

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