Pointing, Acquisition, and Tracking Systems for Free-Space Optical Communication Links

Pointing, acquisition, and tracking (PAT) systems have been widely applied in many applications, from short-range (e.g. human motion tracking) to long-haul (e.g. missile guidance) systems. This dissertation extends the PAT system into new territory: free space optical (FSO) communication system alignment, the most important missing ingredient for practical deployment. Exploring embedded geometric invariances…


1 Introduction
1.1 Overview of Free Space Optical Communications
1.1.1 Last Mile Problem and Its Solutions
1.1.2 Last Mile Networks
1.2 Overview of Pointing, Acquisition, and Tracking Systems
1.2.1 Previous Work
1.2.2 Fine Angular Pointing, Acquisition, and Tracking Systems Enhanced FSO Transceivers Introduction to Transceiver Alignment Limitations and Challenges
1.2.3 Coarse Angular Pointing, Acquisition, and Tracking Systems Camera Based Systems GPS and ISS Hybrid Systems
1.3 Organization
2 Theoretical Studies of Fine Angular Pointing, Acquisition, and Tracking System
2.1 Optical Imaging Theory
2.1.1 Paraxial Imaging
2.1.2 Aberration Theory
2.2 Homography Mapping
2.3 Image Motion Analysis
2.3.1 Pointing Induced Tracking Errors
2.3.2 Minimum Settling Time
2.4 Feedback Controller
2.4.1 Optimal Control
2.4.2 Kalman Estimator Prediction Estimation Steady State Gain
2.5 Feed-Forward Control
2.5.1 Disturbance Rejecter
2.5.2 Reference Follower
3 Design and Analysis of Fine Angular Pointing, Acquisition, and Tracking System
3.1 A PAT System using Focal Plane Motion
3.2 Beam Steerer
3.2.1 Optical Systems Beam Divergence Steering Angle
3.2.2 Actuators Piezo Actuators Piezo-Bender Model Identification Voice Coil Actuators VCA Model Identification
3.3 Angular Resolver
3.3.1 Optical Systems Chief Ray Displacement Image Centroid Shift Compound Parabolic Concentrator
3.3.2 Sensors Operating Principle Signal-To-Noise Ratio Comparison between QD and LEP Sensor Noise Measurement
3.3.3 Calibration
3.4 Control Systems
3.4.1 Axial Decouple
3.4.2 Step Reference Follower Optimal Control Kalman Estimator Step Reference Follower Simulation Step Reference Follower Experiment
3.4.3 Notch Controller Notch Controller Simulation Notch Controller Experiment
3.5 Link Budget Computation
3.6 Summary
4 Theoretical Studies in Coarse Angular Pointing, Acquisition, and Tracking Systems
4.1 Chromatic Aberrations
4.2 Perspective Imaging Theorem and Homogeneous Coordinates . . . . . 109
4.2.1 Homogeneous Coordinates
4.2.2 World Coordinates Transformation
4.3 Estimation Error Propagation
4.3.1 Error Functions Algebraic Distance Mahalanobis and Euclidean Distance
4.3.2 Forward Error Propagation
4.3.3 Backward Error Propagation
4.3.4 Camera Calibration Error Performance Calibration Error Point Transfer Error
4.4 Two-axis Gimbal Model
4.5 Radial Trifocal Tensor
4.5.1 Geometry
4.5.2 Internal Constraints
4.5.3 Radial Trifocal Tensor and Projective Matrices
5 Design and Analysis of Coarse Angular Pointing, Acquisition, and Tracking Systems
5.1 Planar Coarse Pointing Systems
5.1.1 Coarse Pointing Systems For Planar Motion Geometry Calibration/Pointing Algorithm Performance Evaluation
5.1.2 Planar Coarse Pointing Systems For Long Link Distances Intrinsic Matrix Estimation Rotation Matrix Estimation from Homography Rotation Matrix Estimation Through Essential Matrix Optical System Assisted Calibration
5.2 General Coarse Pointing Systems
5.2.1 Geometry
5.2.2 Calibration Stage
5.2.3 Pointing Stage
5.3 Wide Field-Of-View Coarse Pointing Systems
5.3.1 Distortion Correction Algorithm Intrinsic matrix estimation Incorporating the Distortion Model
5.3.2 Performance Evaluation
5.3.3 Enhanced Planar Coarse Pointing Systems
5.4 Wide Field-Of-View and Three Dimensional Pointing Systems
5.4.1 Calibration Stage Algebraic Minimization Scheme (AMS) Geometrical Minimization Scheme (GMS) Performance Evaluation
5.4.2 Pointing Stage Performance evaluation
5.5 Summary
6 Conclusions and Future Work
6.1 Summary of Main Contributions
6.2 Future Work
A Least Squares Optimization
A.1 Least Squares Regression
A.2 Total Least Squares Regression

Author: Ho, Tzung-Hsien

Source: University of Maryland

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