| Description |
1 online resource : illustrations |
| Contents |
880-01 Introduction -- Principle of INS/CNS/GNSS Navigation System -- Filters in Navigation System -- Error Modeling, Calibration and Compensation of Inertial Measurement Unit (IMU) -- Star Map Processing Algorithm of Star Sensor and Autonomous Celestial Navigation -- INS/GNSS Integrated Navigation Method -- INS/CNS INTEGRATED NAVIGATION METHOD -- INS/CNS/GNSS Integrated Navigation Method -- Study for Real-time Ability of INS/CNS/GNSS Integrated Navigation Method -- Semi-physical Simulation Technology of INS/CNS/GNSS Integrated Navigation -- Prospect of INS/CNS/GNSS Integrated navigation technology |
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880-01/(S Machine generated contents note: 1.1. History of INS/CNS/GNSS Navigation -- 1.2. Current Status of INS/CNS/GNSS Navigation Development -- 1.2.1. INS/GNSS Navigation -- 1.2.2. INS/CNS Navigation -- 1.2.3. INS/CNS/GNSS Navigation -- References -- 2.1. Introduction -- 2.2. Coordinate Frames and Earth Reference Model Commonly Used in Navigation -- 2.2.1. Coordinate Frames Used in Navigation -- 2.2.2. Conversion of Coordinate Systems -- 2.2.3. Earth Reference Model -- 2.3. Inertial Navigation System -- 2.3.1. Work Principle of Inertial Navigation System -- 2.3.2. SINS System Error Equation and Error Propagation Characteristics -- 2.4. Satellite Navigation System -- 2.4.1. Operating Principle of Satellite Navigation System -- 2.4.2. Analysis of Error Characteristics for Satellite Navigation System -- 2.5. Celestial Navigation System -- 2.5.1. Autonomous Celestial Positioning Principle -- 2.5.2. Celestial Attitude Determination Principle -- 2.5.3. Star Sensor in CNS and Analysis of Its Error Characteristics -- 2.6. Chapter Conclusion -- References -- 3.1. Introduction -- 3.2. Kalman Filter -- 3.3. Extended Kalman Filter -- 3.3.1. Mathematical Description of Stochastic Nonlinear System -- 3.3.2. Discrete Extended Kalman Filter -- 3.4. Unscented Kalman Filter -- 3.5. Particle Filter -- 3.6. Unscented Particle Filter (UPF) -- 3.7. Predictive Filtering -- 3.8. Federated Filter -- 3.8.1. Structure of Federated Filter -- 3.8.2. Fusion Algorithm -- 3.9. Chapter Conclusion -- References -- 4.1. Introduction -- 4.2. Error Modeling and Compensation of Inertial Sensors -- 4.2.1. Error Model of Gyroscopes -- 4.2.2. Scale Factor Error Modeling of Gyroscope -- 4.2.3. Temperature Error Modeling of Gyroscope -- 4.3. Design, Error Calibration, and Compensation of Inertial Measurement Units -- 4.3.1. Design of Inertial Measurement Units -- 4.3.2. Optimization Six-Position Hybrid Calibration for SINS -- 4.3.3. Integrated Calibration Method for RLG IMU Using a Hybrid Analytic/Kalman Filter Approach -- 4.3.4. Temperature Error Modeling of IMU Based on Neural Network -- 4.4. High Dynamic Strapdown Inertial Algorithm -- 4.4.1. Error Analysis and Gyro Biases Calibration of Analytic Coarse Alignment for Airborne POS -- 4.4.2. Conical Motion Analysis and Evaluation Criteria for Conical Error Compensation Algorithm -- 4.4.3. Improved Single-Subsample Rotating Vector Attitude Algorithm -- 4.5. Chapter Conclusion -- References -- 5.1. Introduction -- 5.2. Star Map Preprocessing Method for Star Sensors -- 5.2.1. Problem Statements -- 5.2.2. Blurred Star Image De-noising -- 5.2.3. Blurred Star Image Restoration -- 5.2.4. Results and Analysis -- 5.2.5. Conclusions -- 5.3. Star Map Identification Method of Star Sensor -- 5.3.1. Introduction -- 5.3.2. Star Recognition Method Based on AAC Algorithm -- 5.3.3. Hybrid Simulation Result and Analysis -- 5.3.4. Conclusions -- 5.4. Celestial Navigation Method Based on Star Sensor and Semi-physical Simulation Verification -- 5.4.1. Introduction -- 5.4.2. Celestial Navigation Measurements and Orbit Dynamic Model -- 5.4.3. UKF Information Fusion Algorithm -- 5.4.4. Simulation Results -- 5.4.5. Conclusions -- 5.5. Chapter Conclusion -- References -- 6.1. Introduction -- 6.2. Principle of Inertial/Satellite Integrated Navigation -- 6.2.1. Combination Mode of Inertial/Satellite Integrated Navigation -- 6.2.2. Basic Principle for InertiaUSatellite Integrated Navigation -- 6.3. Modeling Method of Inertial/Satellite Integrated Navigation System -- 6.3.1. Linear Modeling Method of Inertial/Satellite Integrated Navigation System Based on the Φ Angle -- 6.3.2. Nonlinear Modeling Method of the Inertial/Satellite Integrated Navigation System Based on Quaternion Error -- 6.4. High-Precision InertiaUSatellite Integrated Navigation Method -- 6.4.1. Inertial/Satellite Integrated Navigation Method Based on Mixed Correction -- 6.4.2. Self-Adaptive Feedback Correction Filter Method Based on Observability Normalization Processing Method -- 6.4.3. Inertial/Satellite Outlier-Resistant Integrated Navigation Method Based on Kalman Filtering Innovation Orthogonality -- 6.4.4. Air Maneuvering Alignment Method Based on Observability Analysis and Lever Arm Error Compensation -- 6.4.5. SINS/GPS Integrated Estimation Method Based on Unscented R-T-S Smoothing -- 6.5. Chapter Conclusion -- References -- 7.1. Introduction -- 7.2. Basic Principle of Inertial/Celestial Integrated Navigation -- 7.2.1. Operating Mode of the Inertial/Celestial Integrated Navigation System -- 7.2.2. Combination Mode of Inertial/Celestial Integrated Navigation System -- 7.2.3. Principle of Inertial Component Error Correction Based on Celestial Measurement Information -- 7.3. Modeling Method of Inertial/Celestial Integrated Navigation System -- 7.3.1. State Equation of Inertial/Celestial Integrated Navigation System -- 7.3.2. Measurement Equation of Inertial/Celestial Integrated Navigation System -- 7.4. New Inertial/Celestial Integrated Navigation Method of Ballistic Missile -- 7.4.1. Principle for Initial Position Error Correction of Missile Launching Point Based on Celestial Measurement Information -- 7.4.2. Inertial/Celestial Integrated Navigation Method of Ballistic Missile Based on UKF -- 7.5. Inertial/Celestial Integrated Navigation Method of Lunar Vehicle -- 7.5.1. Strapdown Inertial Navigation Method of Lunar Vehicle -- 7.5.2. Lunar Inertial/Celestial Integrated Navigation Method Based on UPF -- 7.6. Inertial/Celestial Integrated Attitude Determination Method of Satellite -- 7.6.1. Satellite Attitude Determination System Equation -- 7.6.2. Inertia/Celestial Integrated Attitude Determination Method of Piecewise Information Fusion Based on EICF -- 7.6.3. Method of Minimum Parameter Attitude Matrix Estimation of Satellite Based on UKF -- 7.6.4. Interlaced Optimal-REQUEST and Unscented Kalman Filtering for Attitude Determination -- 7.7. Chapter Conclusion -- References -- 8.1. Introduction -- 8.2. Principle of INS/CNS/GNSS Integrated Navigation -- 8.2.1. Basic Principle of INS/CNS/GNSS Integrated Navigation -- 8.2.2. Combination Mode of INS/CNS/GNSS Integrated Navigation -- 8.2.3. Modeling of INS/CNS/GNSS Integrated Navigation System -- 8.3. INS/CNS/GNSS Integrated Navigation Method Based on Federated UKF -- 8.4. Federated Filtering INS/CNS/GNSS Integrated Navigation Method Based on the Optimized Information Distribution Factor -- 8.4.1. Federated Filtering Equation and Information Distribution Process -- 8.4.2. Federated Filtering INS/CNS/GNSS Integrated Navigation Method Based On Information Distribution Factor Optimization -- 8.4.3. Research on FKF Method Based on an Improved Genetic Algorithm for INS/CNS/GNSS Integrated Navigation System -- 8.5. Chapter Conclusion -- References -- 9.1. Introduction -- 9.2. Piecewise Constant System (PWCS) Observability Analysis Theory and Method -- 9.2.1. Observability Analysis Theory of the PWCS -- 9.2.2. Improved System State Degree of Observability Analysis Method Based on Singular Value Decomposition -- 9.3. Dimensionality Reduction Filter Design of INS/CNS Integrated Navigation System Based on the Improved Degree of Observability Analysis -- 9.4. Dimensionality Reduction Filter Design of INS/GNSS Integrated Navigation System Based on the Improved Degree of Observability Analysis -- 9.5. Federated Filter Design of the INS/CNS/GNSS Integrated Navigation System Based on Dimensionality Reduction Filtering -- 9.6. Chapter Conclusion -- References -- 10.1. Introduction -- 10.2. Principle and Composition of Semi-Physical Simulation System of INS/CNS/GNSS Integrated Navigation -- 10.2.1. Principle of Semi-Physical Simulation System of INS/CNS/GNSS Integrated Navigation |
| Summary |
This book not only introduces the principles of INS, CNS and GNSS, the related filters and semi-physical simulation, but also systematically discusses the key technologies needed for integrated navigations of INS/GNSS, INS/CNS, and INS/CNS/GNSS, respectively. INS/CNS/GNSS integrated navigation technology has established itself as an effective tool for precise positioning navigation, which can make full use of the complementary characteristics of different navigation sub-systems and greatly improve the accuracy and reliability of the integrated navigation system. The book offers a valuable reference guide for graduate students, engineers and researchers in the fields of navigation and its control. Dr. Wei Quan, Dr. Jianli Li, Dr. Xiaolin Gong and Dr. Jiancheng Fang are all researchers at the Beijing University of Aeronautics and Astronautics |
| Analysis |
engineering |
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geografische informatiesystemen |
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geographical information systems |
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cartografie |
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mapping |
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ruimtevlucht |
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space flight |
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elektrotechniek |
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electrical engineering |
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simulatiemodellen |
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simulation models |
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Engineering (General) |
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Techniek (algemeen) |
| Notes |
Jointly published with National Defense Industry Press, Beijing |
| Bibliography |
Includes bibliographical references |
| Notes |
Vendor-supplied metadata |
| Subject |
Navigation (Aeronautics)
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Inertial navigation (Aeronautics)
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Artificial satellites in navigation.
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Nautical astronomy.
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Global Positioning System.
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Global Positioning System.
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TECHNOLOGY & ENGINEERING -- Engineering (General)
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Artificial satellites in navigation
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Global Positioning System
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Inertial navigation (Aeronautics)
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Nautical astronomy
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Navigation (Aeronautics)
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| Form |
Electronic book
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| Author |
Li, Jianli, author
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Gong, Xiaolin, author.
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Fang, Jiancheng, author.
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| ISBN |
9783662451595 |
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366245159X |
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3662451581 |
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9783662451588 |
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