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Introduction to Random Signals and Applied Kalman Filtering with MATLAB Exercises 3rd Revised edition [Pehme köide]

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  • Formaat: Paperback / softback, 496 pages, kõrgus x laius x paksus: 254x178x26 mm, kaal: 880 g, Illustrations, Contains 1 Hardback and 1 Diskette
  • Ilmumisaeg: 28-Nov-1996
  • Kirjastus: John Wiley and Sons (WIE)
  • ISBN-10: 0471128392
  • ISBN-13: 9780471128397
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Introduction to Random Signals and Applied Kalman Filtering with MATLAB Exercises 3rd Revised editionSuurem pilt
  • Formaat: Paperback / softback, 496 pages, kõrgus x laius x paksus: 254x178x26 mm, kaal: 880 g, Illustrations, Contains 1 Hardback and 1 Diskette
  • Ilmumisaeg: 28-Nov-1996
  • Kirjastus: John Wiley and Sons (WIE)
  • ISBN-10: 0471128392
  • ISBN-13: 9780471128397
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780471128397.html
Märksõnad:
A textbook for an introductory course focusing on Kalman filtering. Provides the necessary background in random process theory and the response of linear systems to random inputs; assumes a knowledge of linear systems analysis at the junior or senior level of engineering. The second edition was published sometime before 1992. The 3.5" disk for DOS contains software relating to Kalman filtering that is suitable for tutorial but not practical purposes. Annotation c. by Book News, Inc., Portland, Or.

In this updated edition the main thrust is on applied Kalman filtering. Chapters 1-3 provide a minimal background in random process theory and the response of linear systems to random inputs. The following chapter is devoted to Wiener filtering and the remainder of the text deals with various facets of Kalman filtering with emphasis on applications. Starred problems at the end of each chapter are computer exercises. The authors believe that programming the equations and analyzing the results of specific examples is the best way to obtain the insight that is essential in engineering work.
1 Probability and Random Variables: A Review
1(71)
1.1 Random Signals
1(1)
1.2 Intuitive Notion of Probability
2(3)
1.3 Axiomatic Probability
5(6)
1.4 Joint and Conditional Probability
11(4)
1.5 Independence
15(1)
1.6 Random Variables
16(3)
1.7 Probability Distribution and Density Functions
19(2)
1.8 Expectation, Averages, and Characteristic Function
21(4)
1.9 Normal or Gaussian Random Variables
25(4)
1.10 Impulsive Probability Density Functions
29(1)
1.11 Multiple Random Variables
30(6)
1.12 Correlation, Covariance, and Orthogonality
36(2)
1.13 Sum of Independent Random Variables and Tendency Toward Normal Distribution
38(4)
1.14 Transformation of Random Variables
42(7)
1.15 Multivariate Normal Density Function
49(4)
1.16 Linear Transformation and General Properties of Normal Random Variables
53(4)
1.17 Limits, Convergence, and Unbiased Estimators
57(15)
2 Mathematical Description of Random Signals
72(56)
2.1 Concept of a Random Process
72(3)
2.2 Probabilistic Description of a Random Process
75(3)
2.3 Gaussian Random Process
78(1)
2.4 Stationarity, Ergodicity, and Classification of Processes
78(2)
2.5 Autocorrelation Function
80(4)
2.6 Crosscorrelation Function
84(2)
2.7 Power Spectral Density Function
86(5)
2.8 Cross Spectral Density Function
91(1)
2.9 White Noise
92(2)
2.10 Gauss-Markov Process
94(2)
2.11 Random Telegraph Wave
96(2)
2.12 Narrowband Gaussian Process
98(2)
2.13 Wiener or Brownian-Motion Process
100(3)
2.14 Pseudorandom Signals
103(2)
2.15 Determination of Autocorrelation and Spectral Density Functions from Experimental Data
105(6)
2.16 Sampling Theorem
111(2)
2.17 Discrete Fourier Transform and Fast Fourier Transform
113(15)
3 Response of Linear Systems to Random Inputs
128(31)
3.1 Introduction: The Analysis Problem
128(1)
3.2 Stationary (Steady-State) Analysis
129(3)
3.3 Integral Tables for Computing Mean-Square Value
132(2)
3.4 Pure White Noise and Bandlimited Systems
134(1)
3.5 Noise Equivalent Bandwidth
135(2)
3.6 Shaping Filter
137(1)
3.7 Nonstationary (Transient) Analysis--Initial Condition Response
138(2)
3.8 Nonstationary (Transient) Analysis--Forced Response
140(4)
3.9 Discrete-Time Process Models and Analysis
144(3)
3.10 Summary
147(12)
4 Wiener Filtering
159(31)
4.1 The Wiener Filter Problem
159(2)
4.2 Optimization with Respect to a Parameter
161(2)
4.3 The Stationary Optimization Problem--Weighting Function Approach
163(9)
4.4 The Nonstationary Problem
172(5)
4.5 Orthogonality
177(1)
4.6 Complementary Filter
178(3)
4.7 The Discrete Wiener Filter
181(2)
4.8 Perspective
183(7)
5 The Discrete Kalman Filter, State-Space Modeling, and Simulation
190(52)
5.1 A Simple Recursive Example
190(2)
5.2 Vector Description of a Continuous-Time Random Process
192(6)
5.3 Discrete-Time Model
198(12)
5.4 Monte Carlo Simulation of Discrete-Time Systems
210(4)
5.5 The Discrete Kalman Filter
214(6)
5.6 Scalar Kalman Filter Examples
220(5)
5.7 Augmenting the State Vector and Multiple-Input/Multiple-Output Example
225(3)
5.8 The Conditional Density Viewpoint
228(14)
6 Prediction, Applications, and More Basics on Discrete Kalman Filtering
242(47)
6.1 Prediction
242(4)
6.2 Alternative Form of the Discrete Kalman Filter
246(4)
6.3 Processing the Measurement Vector One Component at a Time
250(2)
6.4 Power System Relaying Application
252(4)
6.5 Power Systems Harmonics Determination
256(4)
6.6 Divergence Problems
260(4)
6.7 Off-Line System Error Analysis
264(6)
6.8 Relationship to Deterministic Least Squares and Note on Estimating a Constant
270(5)
6.9 Discrete Kalman Filter Stability
275(2)
6.10 Deterministic Inputs
277(1)
6.11 Real-Time Implementation Issues
278(3)
6.12 Perspective
281(8)
7 The Continuous Kalman Filter
289(23)
7.1 Transition from the Discrete to Continuous Filter Equations
290(3)
7.2 Solution of the Matrix Riccati Equation
293(3)
7.3 Correlated Measurement and Process Noise
296(3)
7.4 Colored Measurement Noise
299(5)
7.5 Suboptimal Error Analysis
304(1)
7.6 Filter Stability in Steady-State Condition
305(1)
7.7 Relationship Between Wiener and Kalman Filters
306(6)
8 Smoothing
312(23)
8.1 Classification of Smoothing Problems
312(1)
8.2 Discrete Fixed-Interval Smoothing
313(4)
8.3 Discrete Fixed-Point Smoothing
317(3)
8.4 Fixed-Lag Smoothing
320(2)
8.5 Forward-Backward Filter Approach to Smoothing
322(13)
9 Linearization and Additional Intermediate-Level Topics on Applied Kalman Filtering
335(57)
9.1 Linearization
335(13)
9.2 Correlated Process and Measurement Noise for the Discrete Filter. Delayed-State Example
348(5)
9.3 Adaptive Kalman Filter (Multiple Model Adaptive Estimator)
353(8)
9.4 Schmidt-Kalman Filter. Reducing the Order of the State Vector
361(6)
9.5 U-D Factorization
367(4)
9.6 Decentralized Kalman Filter
371(6)
9.7 Stochastic Linear Regulator Problem and the Separation Theorem
377(15)
10 More on Modeling: Integration of Noninertial Measurements Into INS
392(27)
10.1 Complementary Filter Methodology
392(4)
10.2 INS Error Models
396(6)
10.3 Damping the Schuler Oscillation with External Velocity Reference Information
402(5)
10.4 Baro-Aided INS Vertical Channel Model
407(3)
10.5 Integrating Position Measurements
410(3)
10.6 Other Integration Considerations
413(6)
11 The Global Positioning System: A Case Study
419(42)
11.1 Description of GPS
419(4)
11.2 The Observables
423(3)
11.3 GPS Error Models
426(6)
11.4 GPS Dynamic Error Models Using Inertially-Derived Reference Trajectory
432(5)
11.5 Stand-Alone GPS Models
437(6)
11.6 Effects of Satellite Geometry
443(2)
11.7 Differential and Kinematic Positioning
445(4)
11.8 Other Applications
449(12)
APPENDIX A Laplace and Fourier Transforms 461(13)
A.1 The One-Sided Laplace Transform 461(3)
A.2 The Fourier Transform 464(2)
A.3 Two-Sided Laplace Transform 466(8)
APPENDIX B Typical Navigation Satellite Geometry 474(4)
APPENDIX C Kalman Filter Software 478(3)
Index 481