AI Direction Finding Signal Count Estimation
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Solution Overview
Problem
Existing direction-finding measurement applications struggle to accurately estimate the number of received signals, especially in scenarios with multiple emitters, due to limitations in current methods that require covariance matrices and eigenvalue decompositions, which are computationally expensive and not suitable for time-critical applications.
Innovation Solution
A computer-implemented method and device that utilizes a pre-trained artificial intelligence algorithm to estimate the number of received signals in direction-finding measurement applications, without the need for covariance matrices or eigenvalue decompositions, by processing input data from direction-finding antennas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods using covariance matrices and eigenvalue decomposition are used to estimate the number of received signals, then measurement precision may be improved, but device complexity and computational cost increase significantly
Solution Approach 1:
The patent replaces the traditional mechanical/mathematical computation system (covariance matrix calculation and eigenvalue decomposition) with an artificial intelligence-based system. The AI algorithm processes the input data directly to estimate the number of received signals, eliminating the need for complex matrix operations and significantly reducing computational complexity while maintaining measurement precision.
2Measurement precision
If traditional methods using covariance matrices and eigenvalue decomposition are used to estimate the number of received signals, then measurement precision may be improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-training the artificial intelligence algorithm offline before actual deployment. During runtime, the pre-trained AI model can rapidly estimate the number of received signals without performing time-consuming eigenvalue decomposition. This separates the computationally intensive training phase from the time-critical measurement phase, significantly reducing processing time while maintaining precision.
3Reliability
If traditional methods using covariance matrices are used, then reliable results can be obtained, but the method is not suitable for devices with limited computational power
Solution Approach 1:
The patent creates a universal solution by developing an AI-based estimation method that can be deployed across different device types with varying computational capabilities. The pre-trained AI model can run on devices with limited computational power, making the solution adaptable and versatile across multiple platforms while maintaining reliable results, unlike traditional methods that require significant computational resources.
Data Source
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AI summary
The present disclosure provides a computer implemented method for estimating the number of received signals in a direction-finding measurement application, the method comprising receiving input data acquired with at least one direction-finding antenna, providing the input data to a pre-trained artificial intelligence algorithm that is trained to determine the number of received signals based on the input data, and outputting the number of received signals determined by the pre-trained artificial intelligence algorithm. Further, the present disclosure provides a respective computer program product, and a respective measurement application device.