Optimal Trajectory Estimation for Missile Defense Applications.

Format:

Book

Author/Creator:

Hough, Michael E.

Series:

Progress in Astronautics and Aeronautics Series

Language:

English

Physical Description:

1 online resource (0 pages)

Edition:

1st ed.

Place of Publication:

Reston : American Institute of Aeronautics & Astronautics, 2024.

Summary:

Optimal Trajectory Estimation for Missile Defense Applications is concerned with the application of modern estimation theory to aerospace trajectory estimation.

Contents:

Cover

Half Title

Title Page

Copyright

Table of Contents

Chapter 1: Introduction

1.1 Historical Perspective

1.2 Primary Design Challenges for Trajectory Estimation

1.3 Real-World Considerations

1.3.1 Nonlinearities

1.3.2 Multiple Targets and Sensors

1.3.3 Maneuvering Target Dynamics

1.3.4 Computational Constraints

1.4 Statistical Evaluation of Filter Performance

1.5 Presentation Overview

References

Chapter 2: Modern Estimation Theory

2.1 Linear Minimum Variance Filter

2.1.1 Expected Value Operator

2.1.2 Simplified Linear Update

2.1.3 Generalized Linear Update

2.1.4 Linear Prediction Models

2.2 Nonlinear Minimum Variance Filter

2.2.1 Nonlinear Filter Update

2.2.2 Iterated Nonlinear Filter Update

2.2.3 Nonlinear Prediction Models

2.3 U-D Factorization Methods

2.3.1 U-D Factorization

2.3.2 U-D Covariance Update

2.4 Multiple Model Estimation

2.5 Nonlinear Batch Filter

2.5.1 Weighted Least-Squares Batch Filter

2.5.2 Iterated Nonlinear Batch Filters

2.6 Estimate Fusion

2.6.1 Formulation

2.6.2 Batch Fusion

2.6.3 Recursive Fusion

2.7 Bayesian Estimation

2.8 Appendix A

Chapter 3: Nonlinear Measurement Models

3.1 Attitude Reference Models

3.2 Radar Triangulation Measurement Models

3.3 Tropospheric Refraction Measurements Using GPS Radio Occultation

3.3.1 Tropospheric Refraction Models Using Geometrical Optics

3.3.2 Bending Angle Measurements from Ray Path Solutions

3.4 Trilateration Estimates and Covariances

3.5 Satellite Infrared Seeker Models

Appendix A - Doppler Shift Dependence on Index of Refraction

Chapter 4: Keplerian Orbit Determination and Prediction

4.1 Nonlinear State Prediction

4.2 Nonlinear Covariance Prediction

4.3 Covariance Fidelity.

4.4 Nonlinear Prediction for Perturbed Keplerian Orbits

4.5 Classical and Modern Orbit Determination

4.6 Appendix

Chapter 5: Measurement Bias Characterization

5.1 Linear BCF

5.2 Modified Kalman Filters

5.2.1 Kalman Filter with Inflated Measurement Variance

5.2.2 &amp

ldquo

Consider&amp

rdquo

Kalman Filter

5.3 Comparisons of Linear Filters

5.3.1 BCF

5.3.2 Kalman Filter with Inflated Measurement Variance

5.3.3 &amp

Filter

5.4 Nonlinear BCF

5.5 Initialization Using a Batch BCF

5.6 Performance Analysis with Measurement Biases

5.7 Summary and Conclusions

Chapter 6: Measurement Bias Estimation

6.1 Prediction Models

6.2 Radar Measurement Models

6.3 Bias Estimation Algorithm

6.4 Performance Analysis

6.5 Summary and Conclusions

Appendix A - Tropospheric Bias Dynamics

Chapter 7: Precise Orbit Determination Using Radar Trilateration

7.1 Trilateration Concept

7.2 BCF for Trilateration

7.3 Initial Orbit Determination for Trilateration

7.3.1 Initial Trilateration Solution

7.3.2 Iterated Nonlinear Batch Filter

7.4 Orbit Determination Accuracy and Covariance Fidelity

7.5 Applications to Ballistic Missile Defense

7.5.1 Radar Angle-Bias Calibration

7.5.2 System-Level Track Accuracy and Covariance Fidelity

7.6 Summary and Conclusions

Chapter 8: Boost Trajectory Estimation

8.1 Nonlinear Prediction Models

8.1.1 Booster Dynamics Model

8.1.2 Booster State and Covariance Prediction

8.1.3 Orbital Prediction

8.2 Batch Initialization

8.2.1 Polynomial Batch Filter

8.2.2 Batch Initialization with Angle-Only Measurements

8.2.3 Batch Initialization with Range and Angle Measurements

8.3 Iteration of the Estimates with Acceleration Constraints.

8.4 Multiple Model Estimation

8.5 Performance Analysis

8.5.1 Angle-Only Sensors

8.5.2 Burnout Estimation with Range-Angle Sensor

8.6 Conclusions

Chapter 9: Reentry Trajectory Estimation

9.1 Maneuvering Reentry Dynamics

9.2 Nonlinear Markov Lift and Drag Models

9.2.1 Lift Dynamics Models

9.2.2 Drag Dynamics Models

9.3 Expected Maneuvers and Statistics

9.3.1 Expected Maneuvers

9.3.2 Acceleration Statistics

9.3.3 Area-to-Mass Ratio Statistics

9.4 Markov Model Parameters

9.4.1 Lift Time Constants

9.4.2 Acceleration PN

9.4.3 Area-to-Mass Ratio PN

9.5 Adaptive Acceleration Filter

9.5.1 Radar Measurement Model

9.5.2 Iterated EKF(9) Updates and Predictions

9.5.3 Maneuver Detection Logic

9.6 Performance Simulations

9.7 Conclusions

9.8.1 Kinematic Identities

9.8.2 Lift Acceleration Commands

9.8.3 Markov Lift Parameters

9.8.4 Markov Drag Parameters

9.8 Appendix A

9.8 Appendix B

Chapter 10: Reentry Acceleration Characterization

10.1 Maneuvering Reentry Dynamics

10.2 Expected Maneuver Statistics

10.2.1 Acceleration Statistics

10.2.2 Area-to-Mass Ratio Statistics

10.3 Acceleration Characterization Filter

10.3.1 Radar Measurement Model

10.3.2 Iterated ACF(6) Updates

10.3.3 Prediction Models

10.4 Performance Simulations

10.5 Conclusions

10.6 Appendix

Chapter 11: Precise Reentry Estimation Using Radar Trilateration

11.1 Maneuvering Reentry Dynamics

11.2 Statistical Acceleration Models

11.3 Recursive Trilateration Filter

11.3.1 Trilateration Measurements

11.3.2 Iterated Trilateration Updates

11.3.3 State and Covariance Prediction Models

11.4 Performance Simulations

11.5 Conclusions

11.6.1 Kinematic Identities

11.6.2 Acceleration Rate (or Jerk) Model.

11.6.3 Lift Acceleration Models

11.6.4 Drag Acceleration Model

Appendix A - Jacobian Partial Derivatives

Chapter 12: Optimal Guidance for Intercept

12.1 Relative Motion Model

12.2 Interceptor Optimal Guidance

12.3 Time-to-Go Selection

12.4 Target Dynamics

12.4.1 Accelerating Targets During Boost

12.4.2 Decelerating Targets During Reentry

12.5 Nonlinear Estimation Algorithm

12.5.1 Nonlinear Prediction Models

12.5.2 Strapdown Seeker Measurements

12.6 Monte Carlo Simulation

12.7 Filter Performance

12.8 Conclusions

Appendix A

Chapter 13: Fast Frequency Estimation

13.1 A New Approach

13.2 Linear Recursive Filter for Frequency Estimation

13.3 Batch Amplitude Estimation

13.4 Undamped Sinusoid with One Frequency

13.5 Damped Sinusoid with One Frequency

13.6 Undamped Sinusoids with Two Frequencies

13.7 FFE for Model Problems

13.8 FFE for Excursion Cases

13.8.1 Nonlinear Periodic Function

13.8.2 Random Constant

13.8.3 Periodicity Checks

13.9 Conclusions

Chapter 14: Tropospheric Bias Estimation

14.1 Background

14.2 Tropospheric Filter Implementation

14.2.1 TIBET Prediction Model

14.2.2 TIBET Measurement Models

14.2.3 TIBET Update Model

14.3 Simplified Bias Estimation

14.4 Performance Analysis

14.5 Conclusions

14.6 Appendix A - Gauss-Markov Tropospheric Models

14.7 Appendix B - Kinematic Bias Identities

Index

Supporting Materials.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Hough, Michael E. Optimal Trajectory Estimation for Missile Defense Applications

ISBN:

9781624107092