Multi-Sensor Fusion
Prentice Hall
978-0-13-901653-0 (ISBN)
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90165-2 Increasingly, applications require computers to interface with the real world and draw data directly from it. These applications range from defense to medicine, manufacturing to environmental health. They all depend on inputs that are noisy, incomplete, and of limited accuracy. This book introduces multi-sensor fusion, which has emerged as the method of choice for implementing robust systems that can handle imperfect inputs. It represents the first broad, practical text on the subject - covering all the technologies and methods associated with multi-sensor fusion, including: *Multidimensional data structures *Techniques for reasoning with uncertainty *Approaches to enhancing system dependability *Working with meta-heuristics The book reflects six years of sensor fusion research for the Office of Naval Research, introducing novel solutions to challenges such as image registration, distributed agreement, and sensor selection. Multi-Sensor Fusion focuses extensively on applications, including neural networks, genetic algorithms, tabu search and simulated annealing. It comes with a set of functioning C programs on disk to implement these applications.This Sensor Fusion Toolkit includes both a standard Kalman filter and the authors' enhanced Distributed Dynamic Sensor Fusion algorithm, which is easier to use and solves more problems.
This is the essential tutorial and reference for any professional or advanced student developing systems that utilize sensor input, including computer scientists, electrical, mechanical and chemical engineers.
I. INTRODUCTION TO SENSOR FUSION.
1. Introduction.
Importance. Sensor Processes. Applications. Summary. Problem Set 1.
II. FOUNDATIONS OF SENSOR FUSION.
2. Sensors.
Mathematical Description. Use of Multiple Sensors. Construction of Reliable Abstract Sensors From Simple Abstract Sensors. Static and Dynamic Networks. Conclusion. Problem Set 2.
3. Mathematical Tools Used.
Algorithms. Linear Algebra. Coordinate Transformations. Rigid Body Motion. Probability. Dependability and Markov Chains. Gaussian Noise. Meta-Heuristics. Summary. Problem Set 3.
4. High-Performance Data Structures: CAD Based.
Boundary Representations. Sweep Presentation. CSG — Constructive Solid Geometry. Wire-Frame Models and the Wing-Edge Data Structure. Surface Patches and Contours. Generalized Cylinders. Summary. Problem Set 4.
5. High-Performance Data Structures: Tessellated.
Sparse Arrays. Simplex Grids of Non-Uniform Sizes. Grayscale and Color Arrays. Occupancy Grids and HIMM Histogram Maps. Summary. Problem Set 5.
6. High-Performance Data Structures: Trees, and Graphs.
2n Trees. Forest of Quadtrees. Translation Invariant Data Structure. Multi-Dimensional Trees. Graphs of Free Space. Description Trees of Polygons. Range and Interval Trees. Summary. Problem Set 6.
7. High-Performance Data Structures: Functions.
Interpolation. Least Squares Estimation. Splines. Bezier Curves and Bi-Cubic Patches. Fourier Transform, Cepstrum and Wavelets. Modal Representation. Summary. Problem Set 7.
8. Representing Ranges and Uncertainty in Data Structures.
Explicit Accuracy Bounds. Probability and Dempster-Shafer Methods. Statistics. Fuzzy Sets. Summary. Problem Set 8.
III. APPLICATIONS TO SENSOR FUSION.
9. Image Registration for Sensor Fusion.
Image Registration Techniques. Problem Statement. Fitness Function. Tabu Search. Genetic Algorithms. Simulated Annealing. Results. Summary.
10. Designing Optimal Sensor Systems within Dependability Bounds.
Applications. Dependability Measures. Optimization Model. Exhaustive Search on the Multidimensional Surface. Experimental Results of the Exhaustive Search Algorithm. Heuristic Methods. Summary.
11. Sensor Fusion and Approximate Agreement.
Byzantine Generals Problem. Approximate Byzantine Matching. Fusion of Contradictory Sensor Information. Performance Comparison. Hybrid Algorithm. Example 1. Example 2. Summary.
12. Kalman Filtering Applied to a Sensor Fusion Problem.
Background. A New Method. A New Technique for Cloud Removal. A Prototype System. Kalman Filter for Scenario 1. Discussion of Results. Summary.
13. Optimal Sensor Fusion Using Range Trees Recursively.
Sensors. Redundancy and Associated Errors. Faulty Sensor Averaging Problem. Interval Trees. Algorithm to Find the Optimal Region. Algorithm Complexity. Comparison with Known Methods. Summary.
14. Distributed Dynamic Sensor Fusion.
Problem Description. New Paradigm for Distributed Dynamic Sensor Fusion. Robust Agreement Using the Optimal Region. A Comparison with Existing Approaches. Experimental Results. Summary.
IV. CASE STUDIES AND CONCLUSION.
15. Sensor Fusion Case Studies.
Levels of Sensor Fusion. Types of Sensors Available. Research Trends. Case Studies. Summary.
16. Conclusion.
Review. Conclusion.
Appendix A. Program Source Code.
References.
Index483.
| Erscheint lt. Verlag | 24.11.1997 |
|---|---|
| Verlagsort | Upper Saddle River |
| Sprache | englisch |
| Gewicht | 1215 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Weitere Themen ► Hardware | |
| Technik ► Elektrotechnik / Energietechnik | |
| ISBN-10 | 0-13-901653-8 / 0139016538 |
| ISBN-13 | 978-0-13-901653-0 / 9780139016530 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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