close
1.

図書
図書
1. Mathematics of Kalman-Bucy filtering. 2nd ed (: U.S. ; : Germany)
P.A. Ruymgaart, T.T. Soong
出版情報: Berlin ; Tokyo : Springer-Verlag, c1988  xii, 170 p. ; 24 cm
シリーズ名: Springer series in information sciences ; 14
所蔵情報: loading…
2.

図書
図書
2. Introduction to random signal analysis and Kalman filtering
Robert Grover Brown
出版情報: New York : Wiley, c1983  ix, 347 p. ; 25 cm
所蔵情報: loading…
3.

図書
図書
3. Introduction to random signals and applied Kalman filtering. 2nd ed. / Robert Grover Brown, Patrick Y.C. Hwang (: [hbk.] ; : [pbk.])
Brown, Robert Grover ; Hwang, Patrick Y. C.
出版情報: New York : Wiley, c1992  x, 502 p. ; 26 cm.
所蔵情報: loading…
4.

図書
図書
4. Kalman filtering theory (pbk.)
A.V. Balakrishnan
出版情報: New York : Optimization Software, Inc., Publications Division, c1984  xii, 222 p. ; 24 cm
シリーズ名: University series in modern engineering
所蔵情報: loading…
5.

図書
図書
5. Mathematical system theory : the influence of R.E. Kalman : a festschrift in honor of professor R.E. Kalman on the occasion of his 60th birthday (: gw ; : us)
Athanasios C. Antoulas, (ed.)
出版情報: Berlin ; New York : Springer-Verlag, c1991  xi, 605 p. ; 24 cm
所蔵情報: loading…
6.

図書
図書
6. Estimation, control, and the discrete Kalman filter (: New York ; : Berlin)
Donald E. Catlin
出版情報: New York ; Tokyo : Springer-Verlag, c1989  xiii, 274 p. ; 25 cm
シリーズ名: Applied mathematical sciences ; v. 71
所蔵情報: loading…
目次情報: 続きを見る
Basic Probability
Minimum Variance Estimation - How the Theory Fits
The Maximum Entropy Principle
Adjoints, Projections, Pseudoinverses
Linear Minimum Variance Estimation
Recursive Linear Estimation (Bayesian Estimation)
The Discrete Kalman Filter
The Linear Quadratic Tracking Problem
Fixed Interval Smoothing
Appendices A-G
Bibliography
Index
Basic Probability
Minimum Variance Estimation - How the Theory Fits
The Maximum Entropy Principle
7.

図書
図書
7. Mathematics of Kalman-Bucy filtering (: gw ; : us)
P.A. Ruymgaart, T.T. Soong
出版情報: Berlin ; Tokyo : Springer-Verlag, 1985  x, 170 p. ; 24 cm
シリーズ名: Springer series in information sciences ; 14
所蔵情報: loading…
8.

図書
図書
8. Fundamentals of Kalman filtering : a practical approach. 4th ed
Paul Zarchan, Howard Musoff
出版情報: Reston, Va. : American Institute of Aeronautics and Astronautics, c2015  xxv, 876 p. ; 24 cm
シリーズ名: Progress in astronautics and aeronautics ; v. 246
所蔵情報: loading…
9.

図書
図書
9. Fundamentals of Kalman filtering : a practical approach. 2nd ed
Paul Zarchan, Howard Musoff
出版情報: Reston, VA : American Institute of Aeronautics and Astronautics, c2005  xxv, 765 p. ; 24 cm
シリーズ名: Progress in astronautics and aeronautics ; v. 208
所蔵情報: loading…
目次情報: 続きを見る
Preface
Introduction
Acknowledgments
Numerical Basics / Chapter 1：
Simple Vector Operations
Simple Matrix Operations
Numerical Integration of Differential Equations
Noise and Random Variables
Gaussian Noise Example
Calculating Standard Deviation
White Noise
Simulating White Noise
State-Space Notation
Fundamental Matrix
Summary
References
Method of Least Squares / Chapter 2：
Overview
Zeroth-Order or One-State Filter
First-Order or Two-State Filter
Second-Order or Three-State Least-Squares Filter
Third-Order System
Experiments with Zeroth-Order or One-State Filter
Experiments with First-Order or Two-State Filter
Experiments with Second-Order or Three-State Filter
Comparison of Filters
Accelerometer Testing Example
Recursive Least-Squares Filtering / Chapter 3：
Making Zeroth-Order Least-Squares Filter Recursive
Properties of Zeroth-Order or One-State Filter
Properties of First-Order or Two-State Filter
Properties of Second-Order or Three-State Filter
Polynomial Kalman Filters / Chapter 4：
General Equations
Derivation of Scalar Riccati Equations
Polynomial Kalman Filter (Zero Process Noise)
Comparing Zeroth-Order Recursive Least-Squares and Kalman Filters
Comparing First-Order Recursive Least-Squares and Kalman Filters
Comparing Second-Order Recursive Least-Squares and Kalman Filters
Comparing Different-Order Filters
Initial Covariance Matrix
Riccati Equations with Process Noise
Example of Kalman Filter Tracking a Falling Object
Revisiting Accelerometer Testing Example
Kalman Filters in a Nonpolynomial World / Chapter 5：
Polynomial Kalman Filter and Sinusoidal Measurement
Sinusoidal Kalman Filter and Sinusoidal Measurement
Suspension System Example
Kalman Filter for Suspension System
Continuous Polynomial Kalman Filter / Chapter 6：
Theoretical Equations
Zeroth-Order or One-State Continuous Polynomial Kalman Filter
First-Order or Two-State Continuous Polynomial Kalman Filter
Second-Order or Three-State Continuous Polynomial Kalman Filter
Transfer Function for Zeroth-Order Filter
Transfer Function for First-Order Filter
Transfer Function for Second-Order Filter
Filter Comparison
Extended Kalman Filtering / Chapter 7：
Drag Acting on Falling Object
First Attempt at Extended Kalman Filter
Second Attempt at Extended Kalman Filter
Third Attempt at Extended Kalman Filter
Drag and Falling Object / Chapter 8：
Problem Setup
Changing Filter States
Why Process Noise Is Required
Linear Polynomial Kalman Filter
Cannon-Launched Projectile Tracking Problem / Chapter 9：
Problem Statement
Extended Cartesian Kalman Filter
Polar Coordinate System
Extended Polar Kalman Filter
Using Linear Decoupled Polynomial Kalman Filters
Using Linear Coupled Polynomial Kalman Filters
Robustness Comparison of Extended and Linear Coupled Kalman Filters
Reference
Tracking a Sine Wave / Chapter 10：
Extended Kalman Filter
Two-State Extended Kalman Filter with a Priori Information
Alternate Extended Kalman Filter for Sinusoidal Signal
Another Extended Kalman Filter for Sinusoidal Model
Satellite Navigation / Chapter 11：
Problem with Perfect Range Measurements
Estimation Without Filtering
Linear Filtering of Range
Using Extended Kalman Filtering
Using Extended Kalman Filtering with One Satellite
Using Extended Kalman Filtering with Constant Velocity Receiver
Single Satellite with Constant Velocity Receiver
Using Extended Kalman Filtering with Variable Velocity Receiver
Variable Velocity Receiver and Single Satellite
Biases / Chapter 12：
Influence of Bias
Estimating Satellite Bias with Known Receiver Location
Estimating Receiver Bias with Unknown Receiver Location and Two Satellites
Estimating Receiver Bias with Unknown Receiver Location and Three Satellites
Linearized Kalman Filtering / Chapter 13：
Falling Object Revisited
Developing a Linearized Kalman Filter
Cannon-Launched Projectile Revisited
Linearized Cartesian Kalman Filter
Miscellaneous Topics / Chapter 14：
Sinusoidal Kalman Filter and Signal-to-Noise Ratio
When Only a Few Measurements Are Available
Detecting Filter Divergence in the Real World
Observability Example
Aiding
Fading-Memory Filter / Chapter 15：
Fading-Memory-Filter Structure and Properties
Radar Tracking Problem
Assorted Techniques for Improving Kalman-Filter Performance / Chapter 16：
Increasing Data Rate
Adding a Second Measurement
Batch Processing
Adaptive Filtering-Multiple Filters
Adaptive Filtering-Single Filter with Variable Process Noise
Fundamentals of Kalman-Filtering Software / Appendix A：
Software Details
MATLAB
True BASIC
Key Formula and Concept Summary / Appendix B：
Overview of Kalman-Filter Operation Principles
Kalman-Filter Gains and the Riccati Equations
Kalman-Filter Gain Logic
Matrix Inverse
Numerical Integration
Postprocessing Formulas
Simulating Pseudo White Noise
Method of Least-Squares Summary
Fading-Memory Filter Summary
Index
Supporting Materials
Preface
Introduction
Acknowledgments
10.

図書
図書
10. Kalman filtering : theory and practice using MATLAB. 2nd ed (: cloth)
Mohinder S. Grewal, Angus P. Andrews
出版情報: New York : Wiley, c2001  xiii, 401 p. ; 25 cm.
所蔵情報: loading…
目次情報: 続きを見る
Preface
Acknowledgments
General Information / 1：
On Kalman Filtering / 1.1：
On Estimation Methods / 1.2：
On the Notation Used in This Book / 1.3：
Summary / 1.4：
Problems
Linear Dynamic Systems / 2：
Chapter Focus / 2.1：
Dynamic Systems / 2.2：
Continuous Linear Systems and Their Solutions / 2.3：
Discrete Linear Systems and Their Solutions / 2.4：
Observability of Linear Dynamic System Models / 2.5：
Procedures for Computing Matrix Exponentials / 2.6：
Random Processes and Stochastic Systems / 2.7：
Probability and Random Variables / 3.1：
Statistical Properties of Random Variables / 3.3：
Statistical Properties of Random Processes / 3.4：
Linear System Models of Random Processes and Sequences / 3.5：
Shaping Filters and State Augmentation / 3.6：
Covariance Propagation Equations / 3.7：
Orthogonality Principle / 3.8：
Linear Optimal Filters and Predictors / 3.9：
Kalman Filter / 4.1：
Kalman--Bucy Filter / 4.3：
Optimal Linear Predictors / 4.4：
Correlated Noise Sources / 4.5：
Relationships between Kalman and Wiener Filters / 4.6：
Quadratic Loss Functions / 4.7：
Matrix Riccati Differential Equation / 4.8：
Matrix Riccati Equation in Discrete Time / 4.9：
Relationships between Continuous and Discrete Riccati Equations / 4.10：
Model Equations for Transformed State Variables / 4.11：
Application of Kalman Filters / 4.12：
Smoothers / 4.13：
Nonlinear Applications / 4.14：
Problem Statement / 5.1：
Linearization Methods / 5.3：
Linearization about a Nominal Trajectory / 5.4：
Linearization about the Estimated Trajectory / 5.5：
Discrete Linearized and Extended Filtering / 5.6：
Discrete Extended Kalman Filter / 5.7：
Continuous Linearized and Extended Filters / 5.8：
Biased Errors in Quadratic Measurements / 5.9：
Application of Nonlinear Filters / 5.10：
Implementation Methods / 5.11：
Computer Roundoff / 6.1：
Effects of Roundoff Errors on Kalman Filters / 6.3：
Factorization Methods for Kalman Filtering / 6.4：
Square-Root and UD Filters / 6.5：
Other Alternative Implementation Methods / 6.6：
Practical Considerations / 6.7：
Detecting and Correcting Anomalous Behavior / 7.1：
Prefiltering and Data Rejection Methods / 7.3：
Stability of Kalman Filters / 7.4：
Suboptimal and Reduced-Order Filters / 7.5：
Schmidt--Kalman Filtering / 7.6：
Memory, Throughput, and Wordlength Requirements / 7.7：
Ways to Reduce Computational Requirements / 7.8：
Error Budgets and Sensitivity Analysis / 7.9：
Optimizing Measurement Selection Policies / 7.10：
Application to Aided Inertial Navigation / 7.11：
MATLAB Software / 7.12：
Notice / A.1：
General System Requirements / A.2：
Diskette Directory Structure / A.3：
MATLAB Software for Chapter 2 / A.4：
MATLAB Software for Chapter 4 / A.5：
MATLAB Software for Chapter 5 / A.6：
MATLAB Software for Chapter 6 / A.7：
MATLAB Software for Chapter 7 / A.8：
Other Sources of Software / A.9：
A Matrix Refresher / Appendix B：
Matrix Forms / B.1：
Matrix Operations / B.2：
Block Matrix Formulas / B.3：
Functions of Square Matrices / B.4：
Norms / B.5：
Cholesky Decomposition / B.6：
Orthogonal Decompositions of Matrices / B.7：
Quadratic Forms / B.8：
Derivatives of Matrices / B.9：
References
Index
Preface
Acknowledgments
General Information / 1：
文献の複写および貸借の依頼を行う
 文献複写・貸借依頼