LEC # | TOPICS | READINGS |
---|---|---|

1 | Introduction Random Signals Intuitive Notion of Probability Axiomatic Probability Joint and Conditional Probability |
Sections 1.1-1.4 |

2 | Independence Random Variables Probability Distribution and Density Functions |
Sections 1.5-1.7 |

3 | Expectation, Averages and Characteristic Function Normal or Gaussian Random Variables Impulsive Probability Density Functions Multiple Random Variables |
Sections 1.8-1.11 |

4 | Correlation, Covariance, and Orthogonality Sum of Independent Random Variables and Tendency Toward Normal Distribution Transformation of Random Variables |
Sections 1.12-1.14 |

5 | Some Common Distributions | |

6 | More Common Distributions Multivariate Normal Density Function Linear Transformation and General Properties of Normal Random Variables |
Sections 1.15, 1.16 |

7 | Linearized Error Propagation | |

8 | More Linearized Error Propagation | |

9 | Concept of a Random Process Probabilistic Description of a Random Process Gaussian Random Process Stationarity, Ergodicity, and Classification of Processes |
Sections 2.1-2.4 |

10 | Autocorrelation Function Crosscorrelation Function |
Sections 2.5, 2.6 |

11 | Power Spectral Density Function Cross Spectral Density Function White Noise |
Sections 2.7-2.9 |

Quiz 1 (Covers Sections 1-11) | ||

12 | Gauss-Markov Process Random Telegraph Wave Wiener or Brownian-Motion Process |
Sections 2.10, 2.11, 2.13 |

13 | Determination of Autocorrelation and Spectral Density Functions from Experimental Data | Section 2.15 |

14 | Introduction: The Analysis Problem Stationary (Steady-State) Analysis Integral Tables for Computing Mean-Square Value |
Sections 3.1-3.3 |

15 | Pure White Noise and Bandlimited Systems Noise Equivalent Bandwidth Shaping Filter |
Sections 3.4-3.6 |

16 | Nonstationary (Transient) Analysis - Initial Condition Response Nonstationary (Transient) Analysis - Forced Response |
Sections 3.7, 3.8 |

17 | The Wiener Filter Problem Optimization with Respect to a Parameter |
Sections 4.1, 4.2 |

18 | The Stationary Optimization Problem - Weighting Function Approach Orthogonality |
Sections 4.3, 4.5 |

19 | Complementary Filter Perspective |
Sections 4.6, 4.8 |

20 | Estimation A Simple Recursive Example |
Section 5.1 |

Quiz 2 (Covers Sections 12-20) | ||

21 | Markov Processes | |

22 | State Space Description Vector Description of a Continuous-Time Random Process Discrete-Time Model |
Sections 5.2, 5.3 |

23 | Monte Carlo Simulation of Discrete-Time Systems The Discrete Kalman Filter Scalar Kalman Filter Examples |
Sections 5.4-5.6 |

24 | Transition from the Discrete to Continuous Filter Equations Solution of the Matrix Riccati Equation |
Sections 7.1, 7.2 |

25 | Divergence Problems | Section 6.6 |

26 | Complementary Filter Methodology INS Error Models Damping the Schuler Oscillation with External Velocity Reference Information |
Sections 10.1-10.3 |

Final Exam |