M.E. Electronics & Comm. Engg:Detection and Estimation Theory

First Semester

Advanced Digital signal Processing
Advanced Optical Communication Systems
Research Methodology
Digital VLSI Design
Microelectronics Technology

Second Semester

Advanced Solid State Devices
Advanced Communication Techniques
Hardware Description Languages


Third Semester

CDMA and GSM Systems
Seminar
Thesis (starts)


Fourth Semester

Thesis


Detection and Estimation Theory

Signals and Systems: System theory, Stochastic process, Gauss Markov models, Representation of Stochastic Process, Likelihood and Sufficiency.

Review of random processes: Review of Probability Theory, Random variable, Two random variables, Moments and conditional statistics, Sequence of random variables, Random Process definition and classification, Stationary and non stationary process, correlation functions, Stochastic Integrals, Fourier transform of random process. Ergodicity and power spectral density, transformation of random process by linear systems. Representation of random processes via sampling, K-L sampling and narrow band representations, Special random processes (White Gaussian Noise, Wiener Levy Processes, Special random processes, Shot noise processes, Markov processes).

Hypothesis Testing: Simple binary hypothesis tests, Decision Criteria, Neyman pearson tests, Bayes Criteria, Receiver operating characteristics, Multiple Hypothesis testing, Composite hypothesis testing, Asymptotic Error rate of LRT for simple hypothesis testing.

Detection Theory: CFAR Detection, Sequential detection, Walds test, Detection of known signals in white noise: the correlation receiver, Detection of known signals in coloured noise, Maximum SNR Criteria. Detection of signals with unknown parameters.

Estimation Theory: Bayes estimation, Real parameter estimation, Maximum likelihood estimation, Cramer Rao inequality, lower bound on the minimum mean square error in estimating a random parameter, Multiple parameter estimation bound on estimation errors of non random variables, General gaussian problem.

Estimation of Waveforms: Linear MMSE of waveforms, Estimation of stationary process: The Wiener Filter, Estimation of non-stationary process: The Kalman Filter, Relation between Kalman and Wiener filters, Non linear estimation.

Applications to Communication & Radar Systems: Digital communication, Spread Spectrum Communication, Radar Systems, Radar Target Models, Target detection, Parameter estimation in radar systems, Dynamic Target tracking.