DS4010 Model Based Signal Processing Syllabus:
DS4010 Model Based Signal Processing Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To know the fundamentals of model based Processing
To get familiar in Discrete Random Signals and systems
To use State-Space Adaptation Algorithms in signal processing
Applied Physics-Based Processors
UNIT I DISCRETE RANDOM SIGNALS AND SYSTEMS
Deterministic Signals and Systems, Spectral Representation of Discrete Signals, Discrete Random Signals, Spectral Representation of Random Signals, Discrete Systems with Random Inputs, ARMAX Models, Lattice Models, Exponential Models, Spatiotemporal Wave Models, State-Space Models, State-Space, ARMAX Equivalence Models, State-Space and Wave Model Equivalence.
UNIT II ESTIMATION THEORY AND MODEL-BASED PROCESSORS
Estimation Theory: Introduction, Minimum Variance Estimation, Least-Squares Estimation, Optimal Signal Estimation, Model-Based Processors: AR MBP, MA MBP, Lattice MBP, ARMAX MBP, Order Estimation for MBP, Case Study: Electromagnetic Signal Processing, Exponential MBP, Wave MBP.
UNIT III LINEAR AND NON-LINEAR STATE-SPACE MODEL-BASED PROCESSORS
Nonlinear State-Space Model-Based Processors: State-Space MBP, Innovations Approach to the MBP, Innovations Sequence of the MBP, Bayesian Approach to the MBP, Tuned MBP, Tuning and Model Mismatch in the MBP, MBP Design Methodology, Nonlinear State-Space Model-Based Processors: Linearized MBP, Extended MBP, Iterated-Extended MBP, Unscented MBP, Case Study: 2D-Tracking Problem.
UNIT IV ADAPTIVE STATE-SPACE MODEL-BASED PROCESSORS
State-Space Adaption Algorithms, Adaptive Linear State-Space MBP, Adaptive Innovations State Space MBP: Innovations Model, RPE Approach Using the Innovations Model, Adaptive Covariance State-Space MBP, Adaptive Nonlinear State-Space MBP, Case Study: AMBP for Ocean Acoustic Sound Speed Inversion: State-Space Forward Propagator, Sound-Speed Estimation: AMBP Development
UNIT V APPLIED PHYSICS-BASED PROCESSORS
MBP for Reentry Vehicle Tracking, MBP for Laser Ultrasonic Inspections, MBP for Structural Failure Detection, MBP for Passive Sonar Direction-of-Arrival and Range Estimation, MBP for Passive Localization in a Shallow Ocean, MBP for Dispersive Waves, MBP for Groundwater Flow.
TOTAL:45 PERIODS
OUTCOMES:
CO1:To be able to understand the fundamentals of model based Processing
CO1: To be able to learn estimation theory and model-based processors
CO3:Can implement the State-Space Adaptation Algorithms
CO4: Can be able to Apply Physics-Based Processors in real time
CO5: Students can become a Model Based Signal Developer
REFERENCES
1. James V. Candy, Model-based signal processing, IEEE Press: Wiley-Inter science, 2006.
2. J. Candy, Signal Processing: The Modern Approach, New York: McGraw-Hill, 1989.
3. S. Kay, Modern Spectral Estimation: Theory and Applications, Englewood Cliffs, NJ: Prentice-Hall, 1999.
4. S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Englewood Cliffs, NJ: Prentice-Hall, 1993.
5. Monson H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley and Sons, Inc, Singapore, 2012.