Statistical Signal Processing

Statistical Signal Processing

@VTU COE

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What you will learn

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Many practical signals are random in nature or modelled as random processes. Statistical Signal Processing involves processing these signals and forms the backbone of modern communication and signal processing systems.This course will the three broad components of statistical signal processing: random signal modelling, estimation theory and detection theory.

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Lec 4 : Random Processes
Preview 37:23
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Lec 5 : Linear Shift Invariant Systems with Random Inputs
Preview 32:53
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Lec 6 : White Noise and Spectral Factorization Theorem
Preview 33:27

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Lec 7 : Linear Models of Random Signals
Preview 38:32
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Lec 8 : Estimation Theory 1
Preview 32:49
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Lec 9 : Estimation Theory 2: MVUE and Cramer Rao Lower Bound
Preview 38:26

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Lec 10 : Cramer Rao Lower Bound 2
Preview 27:06
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Lec 11 : MVUE through Sufficient Statistic
Preview 30:40
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Lec 12 : MVUE through Sufficient Statistic 2
Preview 26:21

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Lec 13 : Method of Moments and Maximum Likelihood Estimators
Preview 29:10
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Lec 14 : Properties of Maximum Likelihood Estimator (MLE)
Preview 25:40
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Lec 15 : Bayesian Estimators
Preview 31:28
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Lec 16 : Bayesian Estimators 2
Preview 26:01

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Lec 17 : Optimal linear filters: Wiener Filter
Preview 29:45
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Lec 18 : FIR Wiener filter
Preview 23:03
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Lec 19 : Non-Causual IIR Wiener Filter
Preview 31:21
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Lec 20 : Causal IIR Wiener Filter
Preview 30:45

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Lec 21: Linear Prediction of Signals 1
Preview 28:00
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Lec 22 : Linear Prediction of Signals 2
Preview 30:15
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Lec 23 : Linear Prediction of Signals 3
Preview 27:03
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Lec 24: Review Assignment 1
Preview 43:38

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Lec 25: Adaptive Filters 1
Preview 33:10
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Lec 26: Adaptive Filters 2
Preview 26:11
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Lec 27: Adaptive Filters 3
Preview 30:23
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Lec 28: Review Assignment 2
Preview 45:56

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Lec 29: Adaptive Filters 4
Preview 32:01
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Lec 30: Adaptive Filters 4(cont.)
Preview 51:44
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Lec 31: Review Assignment 3
Preview 39:40

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Lec 32: Recursive Least Squares (RLS) Adaptive Filter
Preview 43:01
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Lec 33: Recursive Least Squares (RLS) Adaptive Filter - 2
Preview 30:45
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Lec 34: Review Assignment 4
Preview 47:32

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Lec 35: Kalman Filter - 1
Preview 74:05
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Lec 36: Vector Kalman Filter
Preview 49:58

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Lec 37: Linear Models of Random Signals
Preview 38:18
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Lec 38: Review - 1
Preview 68:35
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Lec 39: Review - 2
Preview 39:05

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Live Session 09-02-2021
Preview 21:00
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Live Session 06-05-2020
Preview 65:42
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Meet Your Instructor
@VTU COE
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Instructor
3.2 Rating
2542 Students
759 Courses
About Instructor

VTU is one of the largest Technological Universities in India with 24 years of Tradition of excellence in Engineering & Technical Education, Research and Innovations. It came into existence in the year 1998 to cater the needs of Indian industries for trained technical manpower with practical experience and sound theoretical knowledge.

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Free

  • Course Duration
    25 h 2 m 32 s
  • Course Level
    Intermediate
  • Student Enrolled
    0
  • Language
    English
This Course Includes
  • 25 h 2 m 32 s Video Lectures
  • 0 Quizzes
  • 0 Assignments
  • 0 Downloadable Resources
  • Full Lifetime Access
  • Certificate of Completion