Atnaujinkite slapukų nuostatas

El. knyga: Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods [Taylor & Francis e-book]

(Sichuan University, Chengdu, China), (Sichuan University, PR of China), (Sichuan University, China), (Sichuan University, PR of China), (Sichuan University, China)
  • Formatas: 440 pages, 18 Tables, black and white; 79 Illustrations, black and white
  • Išleidimo metai: 05-Jul-2012
  • Leidėjas: CRC Press Inc
  • ISBN-13: 9780429104756
Kitos knygos pagal šią temą:
  • Taylor & Francis e-book
  • Kaina: 166,18 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Standartinė kaina: 237,40 €
  • Sutaupote 30%
Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical MethodsDidesnis paveikslėlis
  • Formatas: 440 pages, 18 Tables, black and white; 79 Illustrations, black and white
  • Išleidimo metai: 05-Jul-2012
  • Leidėjas: CRC Press Inc
  • ISBN-13: 9780429104756
Kitos knygos pagal šią temą:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Partnerių interneto svetainė: https://www.taylorfrancis.com/books/9780429104756
Due to the increased capability, reliability, robustness, and survivability of systems with multiple distributed sensors, multi-source information fusion has become a crucial technique in a growing number of areasincluding sensor networks, space technology, air traffic control, military engineering, agriculture and environmental engineering, and industrial control. Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods presents advanced mathematical descriptions and methods to help readers achieve more thorough results under more general conditions than what has been possible with previous results in the existing literature.

Examining emerging real-world problems, this book summarizes recent research developments in problems with unideal and uncertain frameworks. It presents essential mathematical descriptions and methods for multisensory decision and estimation fusion. Deriving thorough results under general conditions, this reference book:











Corrects several popular but incorrect results in this area with thorough mathematical ideas Provides advanced mathematical methods, which lead to more general and significant results Presents updated systematic developments in both multisensor decision and estimation fusion, which cannot be seen in other existing books Includes numerous computer experiments that support every theoretical result

The book applies recently developed convex optimization theory and high efficient algorithms in estimation fusion, which opens a very attractive research subject on minimizing Euclidean error estimation for uncertain dynamic systems. Supplying powerful and advanced mathematical treatment of the fundamental problems, it will help to greatly broaden prospective applications of such developments in practice.
Preface xiii
Acknowledgment xix
1 Introduction
1(28)
1.1 Fundamental Problems
2(1)
1.2 Core of Fundamental Theory and General Mathematical Ideas
3(1)
1.3 Classical Statistical Decision
4(7)
1.3.1 Bayes Decision
5(3)
1.3.2 Neyman-Pearson Decision
8(1)
1.3.2.1 Neyman-Pearson Criterion
8(2)
1.3.3 Minimax Decision
10(1)
1.4 Linear Estimation and Kalman Filtering
11(6)
1.5 Basics of Convex Optimization
17(12)
1.5.1 Convex Optimization
17(1)
1.5.1.1 Basic Terminology of Optimization
17(5)
1.5.2 Duality
22(2)
1.5.3 Relaxation
24(1)
1.5.3.1 5-Procedure Relaxation
24(2)
1.5.3.2 SDP Relaxation
26(3)
2 Parallel Statistical Binary Decision Fusion
29(30)
2.1 Optimal Sensor Rules for Binary Decision Given Fusion Rule
30(15)
2.1.1 Formulation for Bayes Binary Decision
30(1)
2.1.2 Formulation of Fusion Rules via Polynomials of Sensor Rules
31(2)
2.1.3 Fixed-Point Type Necessary Condition for the Optimal Sensor Rules
33(4)
2.1.4 Finite Convergence of the Discretized Algorithm
37(8)
2.2 Unified Fusion Rule
45(8)
2.2.1 Expression of the Unified Fusion Rule
45(3)
2.2.2 Numerical Examples
48(1)
2.2.2.1 Two Sensors
48(2)
2.2.2.2 Three Sensors
50(2)
2.2.2.3 Four Sensors
52(1)
2.3 Extension to Neyman---Pearson Decision
53(6)
2.3.1 Algorithm Searching for Optimal Sensor Rules
56(1)
2.3.2 Numerical Examples
57(2)
3 General Network Statistical Decision Fusion
59(104)
3.1 Elementary Network Structures
60(4)
3.1.1 Parallel Network
60(2)
3.1.2 Tandem Network
62(2)
3.1.3 Hybrid (Tree) Network
64(1)
3.2 Formulation of Fusion Rule via Polynomials of Sensor Rules
64(5)
3.3 Fixed-Point Type Necessary Condition for Optimal Sensor Rules
69(2)
3.4 Iterative Algorithm and Convergence
71(3)
3.5 Unified Fusion Rule
74(10)
3.5.1 Unified Fusion Rule for Parallel Networks
75(3)
3.5.2 Unified Fusion Rule for Tandem and Hybrid Networks
78(1)
3.5.3 Numerical Examples
79(1)
3.5.3.1 Three-Sensor System
80(2)
3.5.3.2 Four-Sensor System
82(2)
3.6 Optimal Decision Fusion with Given Sensor Rules
84(12)
3.6.1 Problem Formulation
85(2)
3.6.2 Computation of Likelihood Ratios
87(1)
3.6.3 Locally Optimal Sensor Decision Rules with Communications among Sensors
88(2)
3.6.4 Numerical Examples
90(1)
3.6.4.1 Two-Sensor Neyman---Pearson Decision System
91(1)
3.6.4.2 Three-Sensor Bayesian Decision System
91(5)
3.7 Simultaneous Search for Optimal Sensor Rules and Fusion Rule
96(24)
3.7.1 Problem Formulation
96(3)
3.7.2 Necessary Conditions for Optimal Sensor Rules and an Optimal Fusion Rule
99(4)
3.7.3 Iterative Algorithm and Its Convergence
103(7)
3.7.4 Extensions to Multiple-Bit Compression and Network Decision Systems
110(1)
3.7.4.1 Extensions to the Multiple-Bit Compression
110(2)
3.7.4.2 Extensions to Hybrid Parallel Decision System and Tree Network Decision System
112(4)
3.7.5 Numerical Examples
116(1)
3.7.5.1 Two Examples for Algorithm 3.2
116(3)
3.7.5.2 An Example for Algorithm 3.3
119(1)
3.8 Performance Analysis of Communication Direction for Two-Sensor Tandem Binary Decision System
120(23)
3.8.1 Problem Formulation
122(1)
3.8.1.1 System Model
122(1)
3.8.1.2 Bayes Decision Region of Sensor 2
122(5)
3.8.1.3 Bayes Decision Region of Sensor 1 (Fusion Center)
127(1)
3.8.2 Bayes Cost Function
128(1)
3.8.3 Results
129(11)
3.8.4 Numerical Examples
140(3)
3.9 Network Decision Systems with Channel Errors
143(20)
3.9.1 Some Formulations about Channel Error
144(1)
3.9.2 Necessary Condition for Optimal Sensor Rules Given a Fusion Rule
145(4)
3.9.3 Special Case: Mutually Independent Sensor Observations
149(2)
3.9.4 Unified Fusion Rules for Network Decision Systems
151(1)
3.9.4.1 Network Decision Structures with Channel Errors
151(3)
3.9.4.2 Unified Fusion Rule in Parallel Bayesian Binary Decision System
154(1)
3.9.4.3 Unified Fusion rules for General Network Decision Systems with Channel Errors
155(2)
3.9.5 Numerical Examples
157(1)
3.9.5.1 Parallel Bayesian Binary Decision System
157(2)
3.9.5.2 Three-Sensor Decision System
159(4)
4 Some Uncertain Decision Combinations
163(28)
4.1 Representation of Uncertainties
164(1)
4.2 Dempster Combination Rule Based on Random Set Formulation
165(12)
4.2.1 Dempster's Combination Rule
167(1)
4.2.2 Mutual Conversion of the Basic Probability Assignment and the Random Set
167(1)
4.2.3 Combination Rules of the Dempster---Shafer Evidences via Random Set Formulation
168(1)
4.2.4 All Possible Random Set Combination Rules
169(2)
4.2.5 Correlated Sensor Basic Probabilistic Assignments
171(1)
4.2.6 Optimal Bayesian Combination Rule
172(2)
4.2.7 Examples of Optimal Combination Rule
174(3)
4.3 Fuzzy Set Combination Rule Based on Random Set Formulation
177(11)
4.3.1 Mutual Conversion of the Fuzzy Set and the Random Set
178(1)
4.3.2 Some Popular Combination Rules of Fuzzy Sets
179(2)
4.3.3 General Combination Rules
181(1)
4.3.3.1 Using the Operations of Sets Only
182(1)
4.3.3.2 Using the More General Correlation of the Random Variables
183(1)
4.3.4 Relationship between the t-Norm and Two-Dimensional Distribution Function
184(2)
4.3.5 Examples
186(2)
4.4 Hybrid Combination Rule Based on Random Set Formulation
188(3)
5 Convex Linear Estimation Fusion
191(50)
5.1 LMSE Estimation Fusion
192(8)
5.1.1 Formulation of LMSE Fusion
192(3)
5.1.2 Optimal Fusion Weights
195(5)
5.2 Efficient Iterative Algorithm for Optimal Fusion
200(12)
5.2.1 Appropriate Weighting Matrix
201(3)
5.2.2 Iterative Formula of Optimal Weighting Matrix
204(1)
5.2.3 Iterative Algorithm for Optimal Estimation Fusion
205(5)
5.2.4 Examples
210(2)
5.3 Recursion of Estimation Error Covariance in Dynamic Systems
212(2)
5.4 Optimal Dimensionality Compression for Sensor Data in Estimation Fusion
214(10)
5.4.1 Problem Formulation
215(1)
5.4.2 Preliminary
216(2)
5.4.3 Analytic Solution for Single-Sensor Case
218(2)
5.4.4 Search for Optimal Solution in the Multisensor Case
220(1)
5.4.4.1 Existence of the Optimal Solution
220(1)
5.4.4.2 Optimal Solution at a Sensor While Other Sensor Compression Matrices Are Given
221(2)
5.4.5 Numerical Example
223(1)
5.5 Quantization of Sensor Data
224(17)
5.5.1 Problem Formulation
227(2)
5.5.2 Necessary Conditions for Optimal Sensor Quantization Rules and Optimal Linear Estimation Fusion
229(6)
5.5.3 Gauss---Seidel Iterative Algorithm for Optimal Sensor Quantization Rules and Linear Estimation Fusion
235(2)
5.5.4 Numerical Examples
237(4)
6 Kalman Filtering Fusion
241(82)
6.1 Distributed Kalman Filtering Fusion with Cross-Correlated Sensor Noises
243(11)
6.1.1 Problem Formulation
244(2)
6.1.2 Distributed Kalman Filtering Fusion without Feedback
246(3)
6.1.3 Optimality of Kalman Filtering Fusion with Feedback
249(1)
6.1.3.1 Global Optimality of the Feedback Filtering Fusion
250(1)
6.1.3.2 Local Estimate Errors
251(1)
6.1.3.3 The Advantages of the Feedback
252(2)
6.2 Distributed Kalman Filtering Fusion with Singular Covariances of Filtering Error and Measurement Noises
254(7)
6.2.1 Equivalence Fusion Algorithm
255(1)
6.2.2 LMSE Fusion Algorithm
255(2)
6.2.3 Numerical Examples
257(4)
6.3 Optimal Kalman Filtering Trajectory Update with Unideal Sensor Messages
261(15)
6.3.1 Optimal Local-Processor Trajectory Update with Unideal Measurements
262(1)
6.3.1.1 Optimal Local-Processor Trajectory Update with Addition of OOSMs
263(4)
6.3.1.2 Optimal Local-Processor Trajectory Update with Removal of Earlier Measurement
267(1)
6.3.1.3 Optimal Local-Processor Trajectory Update with Sequentially Processing Unideal Measurements
268(1)
6.3.1.4 Numerical Examples
269(2)
6.3.2 Optimal Distributed Fusion Trajectory Update with Local-Processor Unideal Updates
271(1)
6.3.2.1 Optimal Distributed Fusion Trajectory Update with Addition of Local OOSM Update
272(2)
6.3.2.2 Optimal Distributed State Trajectory Update with Removal of Earlier Local Estimate
274(1)
6.3.2.3 Optimal Distributed Fusion Trajectory Update with Sequential Processing of Local Unideal Updates
275(1)
6.4 Random Parameter Matrices Kalman Filtering Fusion
276(9)
6.4.1 Random Parameter Matrices Kalman Filtering
276(2)
6.4.2 Random Parameter Matrices Kalman Filtering with Multisensor Fusion
278(3)
6.4.3 Some Applications
281(1)
6.4.3.1 Application to Dynamic Process with False Alarm
281(1)
6.4.3.2 Application to Multiple-Model Dynamic Process
282(3)
6.5 Novel Data Association Method Based on the Integrated Random Parameter Matrices Kalman Filtering
285(18)
6.5.1 Some Traditional Data Association Algorithms
285(2)
6.5.2 Single-Sensor DAIRKF
287(5)
6.5.3 Multisensor DAIRKF
292(3)
6.5.4 Numerical Examples
295(8)
6.6 Distributed Kalman Filtering Fusion with Packet Loss/Intermittent Communications
303(20)
6.6.1 Traditional Fusion Algorithms with Packet Loss
303(1)
6.6.1.1 Sensors Send Raw Measurements to Fusion Center
304(1)
6.6.1.2 Sensors Send Partial Estimates to Fusion Center
304(1)
6.6.1.3 Sensors Send Optimal Local Estimates to Fusion Center
305(1)
6.6.2 Remodeled Multisensor System
306(4)
6.6.3 Distributed Kalman Filtering Fusion with Sensor Noises Cross-Correlated and Correlated to Process Noise
310(3)
6.6.4 Optimal Distributed Kalman Filtering Fusion with Intermittent Sensor Transmissions or Packet Loss
313(4)
6.6.5 Suboptimal Distributed Kalman Filtering Fusion with Intermittent Sensor Transmissions or Packet Loss
317(6)
7 Robust Estimation Fusion
323(72)
7.1 Robust Linear MSE Estimation Fusion
324(6)
7.2 Minimizing Euclidean Error Estimation Fusion for Uncertain Dynamic System
330(35)
7.2.1 Preliminaries
333(1)
7.2.1.1 Problem Formulation of Centralized Fusion
333(2)
7.2.1.2 State Bounding Box Estimation Based on Centralized Fusion
335(1)
7.2.1.3 State Bounding Box Estimation Based on Distributed Fusion
336(1)
7.2.1.4 Measures of Size of an Ellipsoid or a Box
337(1)
7.2.2 Centralized Fusion
338(13)
7.2.3 Distributed Fusion
351(5)
7.2.4 Fusion of Multiple Algorithms
356(1)
7.2.5 Numerical Examples
357(1)
7.2.5.1 Figures 7.4 through 7.7 for Comparisons between Algorithms 7.1 and 7.2
358(5)
7.2.5.2 Figures 7.8 through 7.10 for Fusion of Multiple Algorithms
363(2)
7.3 Minimized Euclidean Error Data Association for Uncertain Dynamic System
365(30)
7.3.1 Formulation of Data Association
368(1)
7.3.2 MEEDA Algorithms
368(10)
7.3.3 Numerical Examples
378(17)
References 395(12)
Index 407
Yunmin Zhu, Jie Zhou
Pastovi nuoroda: https://www.kriso.lt/db/9780429104756_pe.html
Raktažodžiai: