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ECED 6530: Random Processes and Estimation Theory
Instructor: Dr. Christian Schlegel, FIEEE, Professor and NSERC Chair
Probability theory: mathematical model, conditional probabilities, random variables, cdf and pdf, transformation of random variables, conditional densities, statistical averages. Random processes concept; ensemble, stationarity, ergodicity, correlation and covariance, power spectral density, calculation and measurement of ACF, AVF and PSD, Gaussian random processes, noise. Transmission of random processes through linear systems: time-invariant systems, non-stationary processes, random walks.
Estimation theory: basics, best estimators, conditional expectation, parametric estimation theory, Fisher information, Cramer-Rao lower bound, Bayesian estimation, minimum mean-square error estimation (MMSE), recursive MMSE formulations, Kalman filtering concepts, the extended Kalman filter and applications, the unscented transform, unscented Kalman filter, particle filtering.
Prerequisites: Background information, Pages 1-12 of course notes
Project Study, report in a paper of length no more then 5 pages (conference style).
Dal Course Fall 2017:
We meet Tuesdays 11:30 am -- 2:30 pm (or as announced in class), starting September 5, 2017. Please subscribe to the Twitter feed @UmdccChair for up-to-date information. See Dalhousie class schedule for details: Dal Calendar
Below is an archive of past student project report. Those marked with ♦ are A+ contributions.
Past Student Projects (2016):
Kalman Filters for Robotic Vehicle Localization (by Marco Andreetto) ♦
Particle Filtering applied to diffusion MRI data: Particle Filtering Tractography (by Guglia Berto) ♦
Kalman Filtering: Problem Statement, Simulation and Real Application for Tracking Objects in Videos (by Mattia Bonomi) ♦
A Sequential Unscented Minimum Mean Square Error Estimator for Finding Avalanche Buried (by Mirko Brentari) ♦
Random Data Arrival Analysis in Coupling Data Transmission for Multiple-Access Communication (by Farshid Vanosfaderani)
An implementation of the classical estimation theory in a special case - Using WiTrack to locate the moving target (Jiacheng Guo)
Unscented Kalman Filter for MIMO Performance Analysis (Sagar Basavaraju)
Past Student Projects (2015):
Comparison of four algorithms (SLIM, Basis Pursuit Sparse Estimation, LSE and Windowing based method) for channel estimation (Zahra Alavizadeh)
Minimum Mean Square Equalization techniques applicable for underwater acoustic channels (Afolarin Engwande)
Data Detection and Noise Minimization for Receivers in Underwater Acoustic Environments (Nitisha Sharma)
Past Student Projects (2014):
Application of Autoregressive Models in WSSUS Channel Estimation (Habib Mirhedayati Roudsari)
Diffraction on Random Surfaces (Jan Chochol)
Automatic Modulation Detection (Alireza Karami)
Stochastic Job Shop Scheduling (Milad Akrami)
Past Student Projects (2014):
Sparse Estimation and the Cramer-Rao Lower Bound (David McNutt) ♦
Random Channel Estimation using Wiener Filtering (Paul Dickson)
A Derivation of Applying Random Network Coding (Zichao Zhou)