Non-Uniform Array Beamforming With Adaptive Genetic Weights
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Solution Overview
Problem
Traditional beamforming techniques struggle with non-uniform antenna arrays, particularly those with unknown or damaged elements, as they require a priori knowledge of array uniformity and cannot effectively adjust weights to achieve specified beam patterns.
Innovation Solution
Utilizing machine learning algorithms, specifically genetic algorithms, to determine and apply weights dynamically to non-uniform antenna arrays, compensating for unknown or damaged elements to achieve desired beam patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional beamforming techniques are used with non-uniform arrays, then the system requires a priori knowledge of array uniformity and cannot handle damaged elements, but this limits the adaptability and reliability of the system
Solution Approach 1:
The patent applies parameter changes by using machine learning algorithms to dynamically adjust the weights of antenna elements based on observed signal characteristics. Instead of requiring fixed a priori knowledge of array uniformity, the system adapts weights in real-time to compensate for non-uniformities and damaged elements, transforming the static weight configuration into a dynamic, adaptive parameter set that responds to actual array conditions
Solution Approach 2:
The system implements self-service by using the received signals themselves to determine the appropriate weights for beamforming. The machine learning algorithm processes the actual signal data from the non-uniform array and automatically adjusts weights to achieve desired beam patterns, eliminating the need for external calibration or manual intervention to characterize array non-uniformities
2Adaptability or versatility
If machine learning algorithms are used to determine weights dynamically, then the system can handle non-uniform arrays without a priori knowledge, but this increases device complexity
Solution Approach 1:
The patent substitutes traditional mechanical or manual weight determination methods with machine learning algorithms. Instead of using fixed lookup tables, manual calibration procedures, or simple adaptive algorithms that require extensive a priori knowledge, the system employs intelligent algorithms that automatically learn and adapt to array characteristics from the signal data itself, reducing the need for complex external calibration systems
3Device complexity
If traditional techniques require a priori knowledge of non-uniformities, then the processing is simpler, but this loses information about actual array conditions and reduces measurement precision
Solution Approach 1:
The system implements feedback by using the actual received signals to continuously monitor and adjust the weights. The machine learning algorithm processes the signal data and uses this feedback to refine weight assignments, ensuring that the beam patterns accurately reflect the true array conditions rather than relying on potentially inaccurate a priori assumptions about non-uniformities
Data Source
AI summary
Systems and methods are provided for generating weights for a non-uniform array. Control circuitry for the non-uniform array may receive an indication that signals transmitted by the non-uniform array should be modified to improve similarity to a specified beam pattern. The control circuitry may select a subset of potential parameters with a first genetic algorithm. Based on these parameters, the control circuitry may generate weights for the non-uniform array with a second genetic algorithm, where each determined weight impacts signal output by an element of the antenna array or other machine learning techniques can be used. The control circuitry may apply the weights to the non-uniform array.