Phased Array Antenna Calibration Using Very Near-Field Probes
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Solution Overview
Problem
Real-time on-site calibration and monitoring of phased array antennas are challenging due to measurement setup effects that interrupt antenna performance, making far-field measurements impractical and conventional near-field measurements complex and costly, especially for precise smart antennas like 5G systems and automobile safety radar systems.
Innovation Solution
A proprietary near-field measurement architecture using enclosed measurement probes and machine-learning algorithms to predict antenna performance from very near-field measurements without interrupting antenna operation, allowing for fixed calibration and monitoring subsystems that reduce system complexity and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If far-field measurement is used for calibration, then radiation performance measurement is accurate, but measurement setup is complex and not suitable for real-time applications
Solution Approach 1:
The patent introduces an electromagnetic field sensor as an intermediary device that indirectly measures antenna performance by detecting electromagnetic field characteristics in the near-field region, avoiding the complexity of far-field measurement setups while enabling real-time monitoring without interrupting antenna operation
Solution Approach 2:
The patent replaces the mechanical movement of probes required in conventional near-field scanning with a stationary sensor that uses electromagnetic field detection and machine learning algorithms to infer antenna performance, eliminating the need for physical probe movement and system interruption
2Area of stationary object
If conventional near-field measurement is used, then measurement size is reduced, but measurement procedure interrupts antenna operation
Solution Approach 1:
The patent enables continuous antenna operation during calibration by using a stationary electromagnetic field sensor that monitors the near-field region without requiring probe movement or system shutdown, maintaining uninterrupted signal transmission and reception
Solution Approach 2:
The antenna system performs self-calibration by using its own transmitted signals to generate the electromagnetic field patterns that the sensor detects, eliminating the need for external measurement equipment that would interrupt operation
3Measurement precision
If very close to antenna measurements are taken, then calibration accuracy is improved, but field prediction becomes too complicated
Solution Approach 1:
The patent introduces machine learning algorithms as an intermediary computational layer that processes the complex relationship between very near-field electromagnetic measurements and antenna performance parameters, simplifying the prediction process while maintaining high calibration accuracy
Solution Approach 2:
The patent transforms the complex electromagnetic field prediction problem into a pattern recognition problem by using machine learning models that learn the relationship between sensor measurements and antenna characteristics, changing the computational approach from physics-based simulation to data-driven prediction
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time, non-intrusive calibration and monitoring of phased array antennas, improving accuracy and reducing costs by using machine-learning to analyze near-field data and adjust phase shifters for optimal radiation patterns without stopping the antenna system.
Implementation Method 1
Performance analysis of antennas can be down by the electromagnetic fields measurement in the near-field region of antennas
Data Source
AI summary
A monitoring/calibrating system is provided based on the amplitude and/or phase of electric and/or magnetic fields measurements. In transmitter mode of AUT, one or more probe sets are placed very close to the AUT around the radiation aperture of AUT to prevent probes effects on the AUT performance. For each amplitude/phase state of AUT controller, the near field data are measured and compared with a previously stored dataset. A successive method can reduce the error by changing the amplitude/phase of AUT controller. Moreover, a machine learning method can be used to classify the errors in near field data, errors in amplitude/phase of AUT controller, and automatically tuning the AUT controller. In receiver mode of AUT, the system is the same but the probe sets are working as transmitters to make a known near field around the AUT.