Neural Network Terminal Pointing Correction
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Solution Overview
Problem
Laser communication systems face significant pointing uncertainty issues during the acquisition phase, leading to prolonged acquisition times due to errors in pointing vectors, which can be exacerbated by environmental factors like cloud blockages and terminal attitude changes, limiting the efficiency and reliability of link establishment.
Innovation Solution
A computer-implemented method using a trained artificial neural network (ANN) to correct pointing errors by inputting data characterizing terminal pointing errors and dependent parameters, with incremental real-time updates and conditioning to avoid exceeding original uncertainty bounds, thereby reducing pointing uncertainty between terminals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If precise terminal pointing calibration during manufacturing is performed, then pointing uncertainty is reduced, but acquisition time is increased due to time-consuming calibration processes and limited accuracy from lab-to-field condition differences
Solution Approach 1:
The system performs preliminary calibration actions by training the neural network offline with gathered pointing data before actual operation. The trained ANN is then deployed to the terminal for real-time pointing error correction, eliminating the need for time-consuming calibration during field operation while maintaining high accuracy across varying environmental conditions
Solution Approach 2:
The patent replaces traditional mechanical calibration systems with an artificial neural network-based computational system. Instead of relying on physical calibration procedures and lookup tables, the system uses machine learning algorithms to predict and correct pointing errors in real-time, significantly reducing acquisition time while improving accuracy
2Area of stationary object
If beam divergence is maximized for acquisition purposes, then acquisition coverage is improved, but power density on the remote terminal decreases making detection more challenging
Solution Approach 1:
The system dynamically changes the beam divergence parameter during operation. During the acquisition phase, the beam divergence is increased to maximize coverage area and ensure the remote terminal is illuminated. Once acquisition is achieved and tracking begins, the beam divergence is reduced to concentrate power density on the remote terminal for optimal detection and communication performance
3Speed
If the search scan speed is maximized, then acquisition time is reduced, but the response times of the remote terminal become a limiting factor
Solution Approach 1:
The system performs preliminary pointing error correction using the trained neural network before initiating the search scan. By pre-correcting the pointing vector based on environmental parameters and historical data, the system narrows the search area significantly, allowing faster scan speeds without compromising reliability since the remote terminal is more likely to be within the corrected pointing cone
4Loss of time
If pointing uncertainty is minimized through traditional calibration, then acquisition time is reduced, but the system fails to adapt to changes in relative terminal position, attitudes, or environmental conditions
Solution Approach 1:
The system implements continuous feedback by collecting real-time pointing error observations during operation and using them to retrain and update the neural network. This feedback loop allows the system to adapt to changes in relative terminal position, attitudes, and environmental conditions, maintaining low acquisition times while improving accuracy over time through incremental learning
Solution Approach 2:
The pointing correction system transitions from a static calibration approach to a dynamic adaptive system. The neural network continuously learns from new data and updates its correction models in real-time, allowing the system to adapt to changing operational conditions while maintaining fast acquisition times
Data Source
AI summary
A system and method for reducing laser communication terminal pointing uncertainty. The method trains an artificial neural network (ANN) with input data characterizing terminal pointing error and dependent parameters. The method inputs the trained ANN a set of data of these dependent parameters with unknown pointing error. The method uses the ANN output to apply corrections to the terminal pointing solution to reduce pointing uncertainty. The method can condition the ANN generated corrections to avoid cases where application of the ANN correction could exceed the original pointing uncertainty. This conditioning includes computing the Euclidean distance between current ANN input parameter values and values in the ANN training dataset, and bounding the allowed magnitude of the ANN pointing correction. The method can train the ANN incrementally during terminal operation for real-time updates or train the ANN offline with gathered data and implement the trained ANN on the terminal for subsequent links.