Phased Array Antenna Optimization via Predicted Element Health
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Solution Overview
Problem
Phased array antennas face performance degradation due to element failure during missions, and existing systems lack the ability to detect or adapt to these failures in real-time, leading to suboptimal performance or mission aborts.
Innovation Solution
A method and apparatus that optimize phased array antenna configurations based on predicted future health states of elements, using particle swarm optimization to configure radiation patterns, allowing for preemptive adjustments to maintain performance even with potential degradations, without requiring additional sensors or processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If phased array antenna elements are used without degradation prediction, then the system is simpler and costs less, but the antenna performance degrades over time due to element failure
Solution Approach 1:
The system performs preliminary action by predicting future health states of antenna elements before actual degradation occurs. The configuration is optimized in advance based on predicted future health states rather than reacting to current or past degradation, allowing the antenna to maintain optimal performance throughout the mission duration.
Solution Approach 2:
The system applies preliminary anti-action by pre-compensating for anticipated element degradation. The optimization process accounts for future health states and adjusts the radiation pattern configuration proactively to counteract the expected performance loss from element failure, rather than allowing degradation to occur and then attempting correction.
2Reliability
If real-time element failure detection and reconfiguration is implemented, then antenna performance can be maintained, but additional sensors and processing resources are required
Solution Approach 1:
Instead of implementing real-time detection and reconfiguration, the system performs the optimization in advance based on predicted future health states. The configuration is calculated beforehand using degradation models and stored for execution, eliminating the need for continuous monitoring sensors and real-time processing resources during the mission.
Solution Approach 2:
The system uses degradation models that simulate or copy the expected behavior of failing elements to predict future health states. These models allow the optimization process to account for element failure without requiring actual sensors to detect the failure, as the model behavior is replicated based on historical and environmental data.
3Productivity
If configuration optimization is performed without considering future degradation, then the initial performance is maximized, but the antenna cannot adapt to element failure during mission
Solution Approach 1:
The system performs configuration optimization in advance, considering multiple predicted future health states. By calculating the optimal configuration before the mission based on anticipated degradation patterns, the system ensures both high initial performance and adaptability to element failure throughout the mission duration.
Solution Approach 2:
The optimization process incorporates dynamic elements by considering time-varying degradation patterns. The system evaluates how element health changes over time and adjusts the configuration accordingly, creating a dynamic adaptation strategy that maintains performance across different mission phases rather than using a static configuration.
Data Source
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AI summary
A method for optimizing a phased array antenna. A predicted future health state for elements in the phased array antenna is identified. A configuration for the elements to use a radiation pattern based on the predicted future health state for the elements taking into account potential degradation of a group of the elements in the predicted future health state is also identified.