Antenna Pointing Control Using Wind and Temperature Prediction
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Solution Overview
Problem
Antennas in open-air environments are affected by gusts and temperature changes, leading to deformation and reduced accuracy and performance due to uneven heating, which existing technologies struggle to correct in real-time.
Innovation Solution
A method and device that establish correspondences between wind and temperature information and fields to predict antenna pointing deviations, using prediction models to adjust antenna pointing in advance to compensate for expected environmental influences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large reflector antennas are used to achieve high radio gain and excellent observation performance, then the antenna size/diameter is increased, but the antenna becomes more susceptible to external environmental factors such as gusts and temperature changes
Solution Approach 1:
The system performs preliminary actions by predicting wind field and temperature field distributions before they cause significant antenna deformation. The prediction model forecasts environmental conditions and their impact on antenna structure, allowing the control system to pre-adjust the antenna pointing direction to compensate for expected deviations, thereby maintaining observation performance despite the large antenna size
Solution Approach 2:
The system implements feedback by continuously monitoring actual antenna pointing deviations caused by environmental factors and using this information to train and refine the prediction model. The model learns from historical data the relationship between environmental conditions and antenna deformation, improving its ability to predict and compensate for future deviations, thus reducing the harmful impact of environmental factors on the large antenna structure
2Adaptability or versatility
If the antenna is placed in open-air environments to enable wide coverage and mobility, then the antenna can be deployed flexibly, but it is exposed to gusts and temperature changes that cause structural deformation and pointing inaccuracies
Solution Approach 1:
The system performs preliminary actions by predicting wind field and temperature field distributions before they cause significant antenna deformation. The prediction model forecasts environmental conditions and their impact on antenna structure, allowing the control system to pre-adjust the antenna pointing direction to compensate for expected deviations, thereby maintaining observation performance despite the large antenna size
Solution Approach 2:
The system implements feedback by continuously monitoring actual antenna pointing deviations caused by environmental factors and using this information to train and refine the prediction model. The model learns from historical data the relationship between environmental conditions and antenna deformation, improving its ability to predict and compensate for future deviations, thus reducing the harmful impact of environmental factors on the large antenna structure
3Measurement precision
If real-time correction of antenna pointing is implemented to compensate for environmental influences, then pointing accuracy is improved, but the system complexity increases due to the need for prediction models and environmental sensing
Solution Approach 1:
The system implements self-service by using the antenna structure itself and the natural environmental fields (wind and temperature) as the objects of study. The prediction model learns from the natural behavior of the antenna under environmental influences without requiring complex external correction mechanisms. The system serves itself by predicting its own deformation patterns and automatically compensating through pointing adjustments, reducing the need for complex external correction hardware
Solution Approach 2:
The system changes parameters by focusing on predicting and compensating for the two most significant environmental parameters: wind field distribution and temperature field distribution. By identifying and modeling the relationship between these key parameters and antenna deformation, the system achieves effective pointing correction without needing to account for all possible environmental variables, thus managing system complexity while maintaining high pointing accuracy
Data Source
AI summary
The present disclosure discloses a method, device and equipment for actively regulating antenna pointing under the influence of environmental factors, and relates to the technical field of antenna performance regulation. By pre-establishing the correspondence between the wind force information and wind field, temperature information and temperature field of the preset antenna points, after obtaining the wind force information and temperature information of the preset antenna points in the current preset period, matching is directly performed based on the correspondences, and the perception of the wind field and temperature field in the current preset period is completed in real time, and then the temperature field and wind field of the antenna at the next moment are predicted based on the second prediction model that pre-learns the evolution relationship of the wind field and the temperature field, and the antenna pointing deviation at the next moment is predicted based on the antenna pointing deviation prediction model that pre-learns the correlation between the wind field and the temperature field and the antenna pointing deviation, so as to achieve early prediction of the antenna pointing deviation, so as to actively regulate the antenna pointing, thereby the antenna can timely point to the detection target at the next moment, and improve the accuracy and performance of the antenna.