Course Details

Subject {L-T-P / C} : EC6613 : Model Based Signal Processing { 3-0-0 / 3}

Subject Nature : Theory

Coordinator : Prof. Upendra Kumar Sahoo

Syllabus

Linear state-space model based processors: State-space MBP, Innovation approach to the model based processor (MBP), Innovation Sequence to MBP, Bayesian approach to MBP, Tuned MBP, Tuning and Model Mismatch in the MBP, MBP Design Methodology, MBP Extensions, MBP Identifier, MBP Deconvolver, StateSpace MBP Design. Non Linear state-space model based processors: Linearized Model Based Processor, Extended MBP, Iterated-Extended Model based Processor, Unscented MBP. Adaptive AR, MA, ARMAX, Exponential Model Processors: Adaption algorithms, All-Zero Adaptive MBP, Pole-Zero Adaptive Processors, Lattice Adaptive MBP Adaptive State-Space Model-Based Processors: State-Space Adaption Algorithm, Adaptive linear State-Space MBP, Adaptive Innovation State-Space MBP, Adaptive Covariance State-Space 248 www.nitrkl.ac.in DEPARTMENT OF ELECTRICAL ENGINEERING MBP, Adaptive Nonlinear State-Space MBP Applied Physics Based Processors: MBP for reentry vehicle tracking: RV simplified Dynamics, Signal Processing Model, Processing of RV Signatures, Flight Data Processing, MBP for Laser Ultrasonic Inspections: Laser Ultrasonic Propagation Modeling, Model-Based Laser Ultrasonic Processing, Laser Ultrasonic Experiment, MBP for Structural Failure Detection: Structural Dynamics Model, Model Based Condition Monitor, Model-Based Monitor Design, MBP Vibration Application.

Course Objectives

Course Outcomes

This course will help one student to analyze physical systems using state space approach.

Essential Reading

Supplementary Reading