Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises

ISBN-10: 0470609699
ISBN-13: 9780470609699
Edition: 4th 2012
List price: $219.99 Buy it from $139.70
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Book details

List price: $219.99
Edition: 4th
Copyright year: 2012
Publisher: John Wiley & Sons, Limited
Publication date: 2/20/2012
Binding: Hardcover
Pages: 400
Size: 7.25" wide x 10.00" long x 0.75" tall
Weight: 1.826

Preface
Random Signals Background
Probability and Random Variables: A Review
Random Signals
Intuitive Notion of Probability
Axiomatic Probability
Random Variables
Joint and Conditional Probability, Bayes Rule and Independence
Continuous Random Variables and Probability Density Function
Expectation, Averages, and Characteristic Function
Normal or Gaussian Random Variables
Impulsive Probability Density Functions
Joint Continuous Random Variables
Correlation, Covariance, and Orthogonality
Sum of Independent Random Variables and Tendency Toward Normal Distribution
Transformation of Random Variables
Multivariate Normal Density Function
Linear Transformation and General Properties of Normal Random Variables
Limits, Convergence, and Unbiased Estimators
A Note on Statistical Estimators
Mathematical Description of Random Signals
Concept of a Random Process
Probabilistic Description of a Random Process
Gaussian Random Process
Stationarity, Ergodicity, and Classification of Processes
Autocorrelation Function
Crosscorrelation Function
Power Spectral Density Function
White Noise
Gauss-Markov Processes
Narrowband Gaussian Process
Wiener or Brownian-Motion Process
Pseudorandom Signals
Determination of Autocorrelation and Spectral Density Functions from Experimental Data
Sampling Theorem
Linear Systems Response, State-Space Modeling, and Monte Carlo Simulation
Introduction: The Analysis Problem
Stationary (Steady-State) Analysis
Integral Tables for Computing Mean-Square Value
Pure White Noise and Bandlimited Systems
Noise Equivalent Bandwidth
Shaping Filter
Nonstationary (Transient) Analysis
Note on Units and Unity White Noise
Vector Description of Random Processes
Monte Carlo Simulation of Discrete-Time Processes
Summary
Kalman Filtering and Applications
Discrete Kalman Filter Basics
A Simple Recursive Example
The Discrete Kalman Filter
Simple Kalman Filter Examples and Augmenting the State Vector
Marine Navigation Application with Multiple-Inputs/Multiple-Outputs
Gaussian Monte Carlo Examples
Prediction
The Conditional Density Viewpoint
Re-cap and Special Note On Updating the Error Covariance Matrix
Intermediate Topics on Kalman Filtering
Alternative Form of the Discrete Kalman Filter - the Information Filter
Processing the Measurements One at a Time
Orthogonality Principle
Divergence Problems
Suboptimal Error Analysis
Reduced-Order Suboptimality
Square-Root Filtering and U-D Factorization
Kalman Filter Stability
Relationship to Deterministic Least Squares Estimation
Deterministic Inputs
Smoothing and Further Intermediate Topics
Classification of smoothing Problems
Discrete Fixed-Interval Smoothing
Discrete Fixed-Point Smoothing
Discrete Fixed-Lag Smoothing
Adaptive Kalman Filter (Multiple Model Adaptive Estimator)
Correlated Process and Measurement Noise for the Discrete Filter-Delayed-State Filter Algorithm
Decentralized Kalman Filtering
Difficulty with Hard-Bandlimited Processes
The Recursive Bayesian Filter
Linearization, Nonlinear Filtering, and Sampling Bayesian Filters
Linearization
The Extended Kalman Filter
"Beyond the Kalman Filter"
The Ensemble Kalman Filter
The Unscented Kalman Filter
The Particle Filter
The "Go-Free" Concept, Complementary Filter, and Aided Inertial Examples
Introduction: Why Go Free of Anything?
Simple GPS Clock Bias Model
Euler/Goad Experiment
Reprise: GPS Clock-Bias Model Revisited
The Complementary Filter
Simple Complementary Filter: Intuitive Method
Kalman Filter Approach-Error Model
Kalman Filter Approach-Total Model
Go-Free Monte Carlo Simulation
INS Error Models
Aiding with Positioning Measurements-INS/DME Measurement Model
Other Integration Considerations and Concluding Remarks
Kalman Filter Applications to the GPS and Other Navigation Systems
Position Determination with GPS
The Observables
Basic Position and Time Process Models
Modeling of Different Carrier Phase Measurements and Ranging Errors
GPS-Aided Inertial Error Models
Communication Link Ranging and Timing
Simultaneous Localization and Mapping (SLAM)
Closing Remarks
Laplace and Fourier Transforms
The Continuous Kalman Filter
Index

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