Neural Network Calibration for Watson-Watt Direction Finding
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Solution Overview
Problem
Direction finding systems, particularly those using the Watson-Watt method, face challenges with biasing errors and require large lookup-tables for calibration, which are cumbersome and inefficient in terms of storage and computational resources.
Innovation Solution
A lightweight calibration method utilizing a neural network configured to mimic the two-argument arctangent function, allowing for rapid calibration through transfer learning, reducing the need for lookup-tables and enabling efficient operation across various environments with minimal storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a lookup-table is used for calibration in Watson-Watt direction finding systems, then calibration accuracy is improved, but storage space requirements and computational overhead increase
Solution Approach 1:
The patent transforms the calibration approach from using a comprehensive lookup-table with extensive calibration data to a neural network model with significantly fewer parameters. The neural network learns the essential calibration relationships through training, achieving accurate direction finding with reduced storage requirements while maintaining calibration precision.
Solution Approach 2:
The patent creates a simplified computational model (neural network) that copies the essential functionality of the lookup-table approach but with reduced complexity. The neural network is trained to replicate the calibration behavior of traditional methods while requiring far less storage space, effectively creating a lightweight version of the calibration system.
2Measurement precision
If a lookup-table is used for calibration, then calibration accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent changes the computational parameters from extensive lookup-table queries to a compact neural network with fewer parameters. The neural network processes calibration data through learned weight matrices and activation functions, reducing computational complexity while maintaining accuracy. This parameter transformation enables faster processing with lower computational overhead.
Solution Approach 2:
The patent performs the complex calibration computations in advance during the neural network training phase. Once trained, the neural network contains pre-learned calibration knowledge that can be applied rapidly during operation without requiring complex real-time computations. This preliminary action shifts computational burden from runtime to training time.
3Measurement precision
If traditional calibration methods are used, then system accuracy is improved, but the calibration process becomes cumbersome and time-consuming
Solution Approach 1:
The patent performs comprehensive calibration computations during the neural network training phase before deployment. The trained model encapsulates calibration knowledge that can be rapidly applied during operation. This preliminary action eliminates the need for time-consuming calibration procedures during field operations, reducing calibration time while maintaining system accuracy.
Solution Approach 2:
The patent transforms the calibration process from iterative adjustments and extensive measurements to a streamlined neural network training process. The neural network learns calibration parameters efficiently through supervised learning, reducing the time required to achieve accurate calibration while maintaining system performance.
Data Source
AI summary
A direction finding system can be configurable to: (i) access a baseline neural network initially configured to imitate behavior of a two-argument arctangent function; (ii) apply transfer learning to the baseline neural network to generate a calibrated neural network, wherein the transfer learning calibrates the baseline neural network to perform Watson-Watt direction finding without utilizing a lookup table for error correction; (iii) access measurement data acquired via the direction finding sensor array; (iv) generate preprocessed data by applying one or more preprocessing operations to the measurement data; (v) utilize the preprocessed data as input to the calibrated neural network; and (vi) output angle of arrival data, the angle of arrival data comprising output of the calibrated neural network.