Table of Contents
Study Materials
Readings
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Brown, Robert Grover, and Patrick Y. C. Hwang. Introduction to Random Signals and Applied Kalman Filtering. New York: John Wiley & Sons, March 1992. ISBN: 0471525685. | 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 |