Table of Contents
Assignments
Assignments
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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 | ASSIGNMENTS |
|---|---|---|
| 1 | Introduction Random Signals Intuitive Notion of Probability Axiomatic Probability Joint and Conditional Probability | Problems 1.1-1.4, 1.8 |
| 2 | Independence Random Variables Probability Distribution and Density Functions | Problems 1.9, 1.10, 1.12-1.14 |
| 3 | Expectation, Averages and Characteristic Function Normal or Gaussian Random Variables Impulsive Probability Density Functions Multiple Random Variables | Problems 1.18-1.20, 1.30, 1.38 |
| 4 | Correlation, Covariance, and Orthogonality Sum of Independent Random Variables and Tendency Toward Normal Distribution Transformation of Random Variables | Problems 1.21-1.24, 1.26 |
| 5 | Some Common Distributions | Problems 1.15, 1.16, 1.27-1.29 |
| 6 | More Common Distributions Multivariate Normal Density Function Linear Transformation and General Properties of Normal Random Variables | Problems 1.33-1.37 |
| 7 | Linearized Error Propagation | Problems A.1, A.6 |
| 8 | More Linearized Error Propagation | Problems A.8, A.13 |
| 9 | Concept of a Random Process Probabilistic Description of a Random Process Gaussian Random Process Stationarity, Ergodicity, and Classification of Processes | Problems 2.9-2.11, A.5 |
| 10 | Autocorrelation Function Crosscorrelation Function | Problems 2.2, 2.12, 2.17, 2.19, 2.20 |
| 11 | Power Spectral Density Function Cross Spectral Density Function White Noise | Problems 2.1, 2.8, 2.14, 2.18, 2.22 |
| Quiz 1 (Covers Sections 1-11) | ||
| 12 | Gauss-Markov Process Random Telegraph Wave Wiener or Brownian-Motion Process | Problems 2.16, 2.21, 2.23-2.25 |
| 13 | Determination of Autocorrelation and Spectral Density Functions from Experimental Data | Problem 2.27 |
| 14 | Introduction: The Analysis Problem Stationary (Steady-State) Analysis Integral Tables for Computing Mean-Square Value | Problems 3.4, 3.5, 3.7 |
| 15 | Pure White Noise and Bandlimited Systems Noise Equivalent Bandwidth Shaping Filter | Problems 3.8, 3.9, 3.17 |
| 16 | Nonstationary (Transient) Analysis - Initial Condition Response Nonstationary (Transient) Analysis - Forced Response | Problems 3.18, 3.21, 3.24 |
| 17 | The Wiener Filter Problem Optimization with Respect to a Parameter | Problems 4.4, 4.5 |
| 18 | The Stationary Optimization Problem - Weighting Function Approach Orthogonality | Problems 4.7, 4.8 |
| 19 | Complementary Filter Perspective | Problems 4.13, 4.14 |
| 20 | Estimation A Simple Recursive Example | Problems A.7, A.9 |
| Quiz 2 (Covers Sections 12-20) | ||
| 21 | Markov Processes | Problems A.14, A.15 |
| 22 | State Space Description Vector Description of a Continuous-Time Random Process Discrete-Time Model | Problems A.10, A.11, A.16 |
| 23 | Monte Carlo Simulation of Discrete-Time Systems The Discrete Kalman Filter Scalar Kalman Filter Examples | Problems 5.1, 5.2 |
| 24 | Transition from the Discrete to Continuous Filter Equations Solution of the Matrix Riccati Equation | Problems 7.1, 7.2 |
| 25 | Divergence Problems | Problems 6.8, 6.9 |
| 26 | Complementary Filter Methodology INS Error Models Damping the Schuler Oscillation with External Velocity Reference Information | Problem 10.3 |
| Final Exam |