| Description |
1 online resource (307 p.) |
| Contents |
Cover -- Title Page -- Copyright -- Contents -- List of Figures -- List of Table -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Introduction -- 1.1 State of a Dynamic System -- 1.2 State Estimation -- 1.3 Construals of Computing -- 1.4 Statistical Modeling -- 1.5 Vision for the Book -- Chapter 2 Observability -- 2.1 Introduction -- 2.2 State-Space Model -- 2.3 The Concept of Observability -- 2.4 Observability of Linear Time-Invariant Systems -- 2.4.1 Continuous-Time LTI Systems -- 2.4.2 Discrete-Time LTI Systems -- 2.4.3 Discretization of LTI Systems |
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2.5 Observability of Linear Time-Varying Systems -- 2.5.1 Continuous-Time LTV Systems -- 2.5.2 Discrete-Time LTV Systems -- 2.5.3 Discretization of LTV Systems -- 2.6 Observability of Nonlinear Systems -- 2.6.1 Continuous-Time Nonlinear Systems -- 2.6.2 Discrete-Time Nonlinear Systems -- 2.6.3 Discretization of Nonlinear Systems -- 2.7 Observability of Stochastic Systems -- 2.8 Degree of Observability -- 2.9 Invertibility -- 2.10 Concluding Remarks -- Chapter 3 Observers -- 3.1 Introduction -- 3.2 Luenberger Observer -- 3.3 Extended Luenberger-Type Observer -- 3.4 Sliding-Mode Observer |
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3.5 Unknown-Input Observer -- 3.6 Concluding Remarks -- Chapter 4 Bayesian Paradigm and Optimal Nonlinear Filtering -- 4.1 Introduction -- 4.2 Bayes' Rule -- 4.3 Optimal Nonlinear Filtering -- 4.4 Fisher Information -- 4.5 Posterior Cramér-Rao Lower Bound -- 4.6 Concluding Remarks -- Chapter 5 Kalman Filter -- 5.1 Introduction -- 5.2 Kalman Filter -- 5.3 Kalman Smoother -- 5.4 Information Filter -- 5.5 Extended Kalman Filter -- 5.6 Extended Information Filter -- 5.7 Divided-Difference Filter -- 5.8 Unscented Kalman Filter -- 5.9 Cubature Kalman Filter -- 5.10 Generalized PID Filter |
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5.11 Gaussian-Sum Filter -- 5.12 Applications -- 5.12.1 Information Fusion -- 5.12.2 Augmented Reality -- 5.12.3 Urban Traffic Network -- 5.12.4 Cybersecurity of Power Systems -- 5.12.5 Incidence of Influenza -- 5.12.6 COVID-19 Pandemic -- 5.13 Concluding Remarks -- Chapter 6 Particle Filter -- 6.1 Introduction -- 6.2 Monte Carlo Method -- 6.3 Importance Sampling -- 6.4 Sequential Importance Sampling -- 6.5 Resampling -- 6.6 Sample Impoverishment -- 6.7 Choosing the Proposal Distribution -- 6.8 Generic Particle Filter -- 6.9 Applications -- 6.9.1 Simultaneous Localization and Mapping |
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6.10 Concluding Remarks -- Chapter 7 Smooth Variable-Structure Filter -- 7.1 Introduction -- 7.2 The Switching Gain -- 7.3 Stability Analysis -- 7.4 Smoothing Subspace -- 7.5 Filter Corrective Term for Linear Systems -- 7.6 Filter Corrective Term for Nonlinear Systems -- 7.7 Bias Compensation -- 7.8 The Secondary Performance Indicator -- 7.9 Second-Order Smooth Variable Structure Filter -- 7.10 Optimal Smoothing Boundary Design -- 7.11 Combination of SVSF with Other Filters -- 7.12 Applications -- 7.12.1 Multiple Target Tracking -- 7.12.2 Battery State-of-Charge Estimation -- 7.12.3 Robotics |
| Notes |
Description based upon print version of record |
|
7.13 Concluding Remarks |
| Form |
Electronic book
|
| Author |
Habibi, Saeid
|
|
Haykin, Simon
|
| ISBN |
9781119078180 |
|
1119078180 |
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