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 |