Detection and Estimation Theory Detailed Syllabus for Wireless and Mobile Communications M.Tech first year first sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.<\/p>\n
The detailed syllabus for Detection and Estimation Theory M.Tech 2017-2018 (R17) first year first sem is as follows.<\/p>\n
M.Tech. I Year I Sem.<\/p>\n
UNIT \u2013I :\u00a0 Random Processes:<\/strong> Discrete Linear Models, Markov Sequences and Processes, Point Processes, and Gaussian Processes.<\/p>\n UNIT \u2013II : Detection Theory:<\/strong> Basic Detection Problem, Maximum A posteriori Decision Rule, Minimum Probability of Error Classifier, Bayes Decision Rule, Multiple-Class Problem (Bayes)- minimum probability error with and without equal a priori probabilities, Neyman-Pearson Classifier, General Calculation of Probability of Error, General Gaussian Problem, Composite Hypotheses.<\/p>\n UNIT \u2013III : Linear Minimum Mean-Square Error Filtering:<\/strong> Linear Minimum Mean Squared Error Estimators, Nonlinear Minimum Mean Squared Error Estimators. Innovations, Digital Wiener Filters with Stored Data, Real-time Digital Wiener Filters, Kalman Filters.<\/p>\n UNIT \u2013IV : Statistics:<\/strong> Measurements, Nonparametric Estimators of Probability Distribution and Density Functions, Point Estimators of Parameters, Measures of the Quality of Estimators, Introduction to Interval Estimates, Distribution of Estimators, Tests of Hypotheses, Simple Linear Regression, Multiple Linear Regression.<\/p>\n UNIT \u2013V : Estimating the Parameters of Random Processes from Data<\/strong>:Tests for Stationarity and Ergodicity, Model-free Estimation, Model-based Estimation of Autocorrelation Functions, Power Special Density Functions.<\/p>\n TEXT BOOKS:<\/strong><\/p>\n REFERENCE BOOKS:<\/strong><\/p>\n\n
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