DS4019 Signal Detection and Estimation Theory Syllabus:
DS4019 Signal Detection and Estimation Theory Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To understand the basics of statistical decision theory used for signal detection and estimation.
To learn the detection of deterministic and random signals using statistical models.
To understand the performance of signal parameters
To learn the basics of multi-user detection theory
To understand Wiener filter and Kalman filter in detail
UNIT I STATISTICAL DECISION THEORY
Gaussian variables and processes, problem formulation and objective of signal detection and signal parameter estimation in discrete-time domain. 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 AND RANDOM SIGNALS
Matched filter detector and its performance; generalized matched filter; detection of sinusoid with known and unknown amplitude, phase, frequency and arrival time, linear model, energy detectors. Detection Of Random Signals: Estimator-correlator, linear model, general Gaussian detection, detection of Gaussian random signal with unknown parameters, weak signal detection.
UNIT III ESTIMATION OF SIGNAL PARAMETERS
Formulation of the General Parameter Estimation Problem, Relationship between Detection and Estimation Theory, Types of Estimation Problems, Properties of Estimators, maximum likelihood estimation, Minimax Estimation ,invariance principle; estimation efficiency; Bayesian estimation: philosophy, nuisance parameters, risk functions, minimum mean square error estimation, maximum a posteriori estimation. Comparison of Estimators of Parameters.
UNIT IV SAMPLE DETECTION AND FILTERS
Hypothesis Testing and the MAP Criterion, Bayes Criterion, Minimax Criterion, Neyman-Pearson Criterion, Sequential Detection, The Optimum Digital Detector in Additive Gaussian Noise , Performance of Binary Receivers in AWGN. Orthogonality Principle, Autoregressive Techniques, Discrete Wiener Filter, Continuous Wiener Filter, Generalization of Discrete and Continuous Filter Representations , Linear Least-Squares Methods, Minimum-Variance Weighted Least-Squares Methods, Minimum-Variance, Least Squares, Kalman Algorithm – Computational Considerations, Signal Estimation, Continuous Kalman Filter, Extended Kalman Filter.
UNIT V APPLICATIONS
Detector Structures in Non-Gaussian Noise , Examples of Noise Models, Receiver Structures, and Error-Rate Performance, Estimation of Non-Gaussian Noise Parameters Fading Multipath Channel Models, Receiver Structures with Known Channel Parameters, Receiver Structures without Knowledge of Phase, Receiver Structures without Knowledge of Amplitude or Phase, Receiver Structures and Performance with No Channel Knowledge. Complex and vector extensions of detectors: known deterministic signal in CWGN, spatially/temporally uncorrelated noise, random signal in CWGN.
PRACTICAL EXERCISES: 30 PERIODS
1. Experiment on maximum likelihood estimation
2. Experiment on Bayesian estimation
3. Experiment on FIR Wiener filter like in linear prediction of speech signals.
4. Experiment on Kalman filtering
5. detection of deterministic signals in Gaussian noise
6. estimation of signal parameters
7. detection of random signals in Gaussian noise
8. Estimation of Non-Gaussian Noise Parameters
9. Performance of Binary Receivers in AWGN
10. Detector Structures and Receiver Structures
COURSE OUTCOMES:
CO1: Acquire basics of statistical decision theory used for signal detection and estimation.
CO2: Examine the detection of deterministic and random signals using statistical models.
CO3: Examine the performance of signal parameters using optimal estimators.
CO4: To design Wiener and Kalman filters to solve linear estimation problems
CO5: designing statistical algorithms for varied applications.
TOTAL:75 PERIODS
REFERENCES:
1. Harry L. Van Trees, Detection, Estimation and Modulation Theory, Part I John Wiley and Sons, New York, 2016.
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.
5. Ludeman, Lonnie C., Random processes: filtering, estimation, and detection, John Wiley & Sons, Inc., 2003
6. Sergio Verdu , MultiUser Detection, Cambridge University Press, 2011.
7. Thomas Schonhoff, Detection and Estimation Theory, Prentice Hall, New Jersey, 2007.
8. Monson H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley and Sons, Inc, Singapore, 2012.