Antenna Site Condition Detection Using AI Sky Plots
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Solution Overview
Problem
The challenge lies in automatically detecting and maintaining optimal antenna site conditions for satellite antennas, which are affected by movable satellites, environmental changes, and potential obstructions, leading to suboptimal signal reception and requiring impractical manual inspections in large networks.
Innovation Solution
A method utilizing image-processing artificial intelligence models, specifically convolutional and recurrent neural networks, to transform signal source observations into images, calculate obstruction vectors, and detect changes in antenna site conditions, enabling automatic alerts and reducing manual labor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of antenna sites is performed, then detailed assessment of antenna conditions can be obtained, but the process becomes impractical and time-consuming for large networks comprising several hundred antennas
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated electronic system that uses signal processing and artificial intelligence algorithms to assess antenna site conditions. The system automatically analyzes signal characteristics, generates sky plots, detects obstructions, and identifies anomalies without human intervention, thereby maintaining high assessment accuracy while eliminating the time-consuming nature of manual inspections across large networks
Solution Approach 2:
The system enables self-assessment of antenna site conditions by automatically collecting signal data, processing it through AI models, generating visual sky plots, and identifying issues such as obstructions or signal degradation. The antenna network essentially inspects itself through this automated feedback mechanism, eliminating the need for external manual inspection resources
2Loss of information
If conventional test equipment is used for antenna inspection, then basic signal reception information can be obtained, but limited information about overall antenna installation conditions is provided
Solution Approach 1:
The patent merges multiple assessment functions into a single integrated system that simultaneously evaluates signal reception quality, sky plot accuracy, obstruction detection, and environmental condition analysis. By combining these previously separate inspection tasks into one unified AI-driven platform, the system provides comprehensive information about overall antenna installation conditions without requiring multiple separate complex devices
Solution Approach 2:
The automated assessment system is designed to perform multiple functions: analyzing signal characteristics, generating sky plots, detecting obstructions, identifying antenna misalignments, and monitoring environmental changes. This multi-functional capability allows a single system to provide complete information about antenna installation conditions, replacing the need for specialized equipment for each specific assessment type
3Reliability
If reactive approach is used for responding to degraded antenna signal reception, then response to critical issues can be made, but proactive maintenance and prevention of signal degradation cannot be achieved
Solution Approach 1:
The system implements continuous automated monitoring of antenna signal reception and environmental conditions, with the AI model analyzing data in real-time and providing feedback about potential issues before they cause signal degradation. The system generates alerts and recommendations for proactive maintenance, enabling the network operator to address problems before they impact service reliability, thus transitioning from reactive to proactive maintenance automation
Data Source
AI summary
A method for automatic detection of antenna site conditions, ASC, at an antenna site, AS, of an antenna, A, the method comprising the steps of providing (S1) signal source observations, SSO, derived from signals received by the antenna, A, from at least one signal source, SS, and transforming (S2) the signal source observations, SSO, into images fed to a trained image-processing artificial intelligence, AI, model which calculates antenna site conditions, ASC, at an antenna site, AS, of the respective antenna, A.