Page 35 - REC :: M.E. CS Curriculum and Syllabus - R2019

PROFESSIONAL ELECTIVE- IV

Subject Code Subject Name Category L T P C
CU19P41 DETECTION AND ESTIMATION THEORY PE 3 0 0 3

Objectives:
 To learn the usage of tools from probability and signal processing domains
 To gain knowledge on detection of deterministic signals
 To obtain optimum detector/estimator for an digital communication system
 To learn the detection of random signals with unknown parameters
 To identify the (error) performance bounds of any detector/estimator adopted in communication systems

UNIT-I STATISTICAL DECISION THEORY 9
Bayesian, minimax, and Neyman-Pearson decision rules, likelihood ratio, receiver operating characteristics, composite
hypothesis testing, locally optimum tests, detector comparison techniques, asymptotic relative efficiency.
UNIT-II DETECTION OF DETERMINISTIC SIGNALS 9
Matched filter detector and its performance; generalized matched filter; detection of sinusoid with unknown
amplitude, phase, frequency and arrival time, linear model
UNIT-III DETECTION OF RANDOM SIGNALS 9
Estimator-correlator, linear model, general Gaussian detection, detection of Gaussian random signal with unknown
parameters, weak signal detection.
UNIT-IV NONPARAMETRIC DETECTION 9
Detection in the absence of complete statistical description of observations, sign detector, Wilcoxon detector, detectors
based on quantized observations, robustness of detectors.
UNIT-V ESTIMATION OF SIGNAL PARAMETERS 9
Minimum variance unbiased estimation, Fisher information matrix, Cramer-Rao bound, sufficient statistics, minimum
statistics, complete statistics; linear models; best linear unbiased estimation; maximum likelihood estimation,
invariance principle; estimation efficiency; Bayesian estimation: philosophy, nuisance parameters, risk functions,
minimum mean square error estimation, maximum a posteriori estimation.
Total Contact Hours : 45

Course Outcomes:
On completion of the course, students will be able to
 State various detection problems in hypotheses testing framework
 Describe various estimation algorithms for their error performance
 Develop algorithms for various estimation problems
 Design various sequential procedures for detection/estimation problems
 Formulate algorithms for tracking

Reference Books(s) / Web links:
1 H. L. Van Trees, "Detection, Estimation and Modulation Theory: Part I, II, and III", John Wiley, NY, 1968.
2 H. V. Poor, "An Introduction to Signal Detection and Estimation", Springer, 2/e, 1998.
3 S. M. Kay, "Fundamentals of Statistical Signal Processing: Estimation Theory", Prentice Hall PTR, 1993.
4 S. M. Kay, "Fundamentals of Statistical Signal Processing: Detection Theory", Prentice Hall PTR, 1998.