EL4072 Signal Detection and Estimation Syllabus:
EL4072 Signal Detection and Estimation Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To understand the concepts of detection and estimation.
To learn the basics of multi-user detection theory
To understand the theory behind various estimation techniques.
To understand Wiener filter and Kalman filter in detail.
UNIT I REVIEW OF PROBABILITY AND STOCHASTIC PROCESS
Conditional Probability, Bayes’ Theorem , Random Variables, Conditional Distributions and Densities, moments and distribution of random variables., Stationary Processes Cyclostationary Processes Averages and Ergodicity Autocorrelation Function Power Spectral Density Discrete Time Stochastic Processes, Spatial Stochastic Processes, Random Signals, Relationship of Power Spectral Density and Autocorrelation Function.
UNIT II SINGLE AND MULTIPLE SAMPLE DETECTION
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.
UNIT III FUNDAMENTALS OF ESTIMATION THEORY
Formulation of the General Parameter Estimation Problem, Relationship between Detection and Estimation Theory, Types of Estimation Problems, Properties of Estimators, Bayes estimation, Minimax Estimation, Maximum-Likelihood Estimation, Comparison of Estimators of Parameters.
UNIT IV WIENER AND KALMAN FILTERS
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.
PRACTICALS: PERIOD – 30
Suggested List of Experiments
Software Requirement: Matlab / Python / Equivalent
1. Power Spectrum Estimation of a Random Signal
2. Maximum Likelihood Estimation
3. Design of optimum receiver in AWGN channel
4. Wiener Filter Design
5. Adaptive Filter Design using LMS algorithm
6. Minimum Variance Estimation
COURSE OUTCOMES:
Upon completion of the course the student will be
CO1:Able to understand the importance of probability and stochastic process concepts in detection and estimation.
CO2: Able to design optimum detector and estimator for AWGN channel
CO3: Able to design and analyze the various estimators.
CO4: Able to design Wiener and Kalman filters to solve linear estimation problems.
CO5: Able to design and develop novel receiver structures suitable for modern technology.
TOTAL:75 PERIODS
REFERENCES
1. Harry L. Van Trees, “Detection, Estimation and Modulation Theory”, Part I John Wiley and Sons, New York, 2004.
2. Ludeman, Lonnie C. Random processes: filtering, estimation, and detection. John Wiley & Sons, Inc., 2003
3. Sergio Verdu “ Multi User Detection” Cambridge University Press, 1998
4. Steven M. Kay, “Fundamentals of Statistical Processing, Volume I: Estimation Theory”, Prentice Hall Signal Processing Series, Prentice Hall, PTR, NewJersy, 1993.
5. Thomas Schonhoff, “Detection and Estimation Theory”, Prentice Hall, NewJersy, 2007.