EW ML Model Updating for Radar Emitter Deinterleaving
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Solution Overview
Problem
Existing EW systems face challenges in efficiently identifying and tracking radar emitters in complex environments, particularly due to the need for post-sortie data analysis and significant human interaction, which is slow and inefficient in responding to advanced threat waveforms.
Innovation Solution
An automated system utilizing machine learning algorithms for real-time identification and tracking of radar emitters, integrating supervised and unsupervised ML to analyze pulse descriptor words, and updating emitter libraries dynamically to adapt to new threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-driven data analysis and logical adjustments are used in EW systems, then accuracy in threat assessment can be achieved, but response time becomes excessively slow and operational efficiency deteriorates
Solution Approach 1:
The system enables automated machine learning model training and updating using data from the dynamic emitter library, eliminating the need for human operators to manually analyze data and adjust algorithms. The system self-updates its threat assessment capabilities by automatically training ML models on new emitter data, thereby maintaining high accuracy while dramatically reducing response time.
Solution Approach 2:
The patent replaces the mechanical process of human data analysis and manual algorithm adjustment with automated machine learning systems. ML models automatically process emitter data, identify patterns, and update threat assessment algorithms without human intervention, substituting human cognitive processes with computational algorithms that operate at machine speed.
2Measurement precision
If extensive databases of known emitters are maintained, then identification accuracy improves, but system complexity and resource requirements increase
Solution Approach 1:
The system implements a dynamic emitter library that automatically updates with new emitter data captured during operations. Rather than maintaining a static, manually curated database, the library dynamically adapts by incorporating new emitters and updating existing profiles through automated data collection and ML-based pattern recognition, reducing manual database management while improving identification accuracy.
Solution Approach 2:
The system establishes a feedback loop where captured emitter data is continuously fed back into the dynamic emitter library and used to retrain ML models. This feedback mechanism automatically improves identification accuracy over time by learning from new data, eliminating the need for manual database updates and reducing system complexity.
3Device complexity
If traditional EW algorithms are used with fixed logic, then system simplicity is maintained, but adaptability to advanced threat waveforms deteriorates
Solution Approach 1:
The system replaces fixed, static EW algorithms with dynamic machine learning models that automatically adapt to new threat waveforms. The ML models are continuously trained on captured emitter data, enabling them to dynamically adjust their detection and classification logic to recognize advanced and evolving threat patterns without requiring manual algorithm updates.
Solution Approach 2:
The system changes the fundamental parameters of the EW algorithm from fixed logical rules to adaptive machine learning models with adjustable parameters. The ML models learn optimal detection parameters automatically from data, enabling the system to adapt to diverse and advanced threat waveforms while maintaining computational efficiency through optimized model architectures.
Data Source
AI summary
A system and method are described for updating Machine Learning (ML) models in an electronic warfare (EW) environment. The ML models are updated automatically post mission using threat data of emitters in the EW environment and then deployed to hardware in an aircraft. The updated ML models are used during a subsequent mission and include an unsupervised ML model to deinterleave waveforms received from the emitters and a supervised ML model for emitter identification, waveform tracking, and anomaly detection based on the deinterleaved waveforms. The ML models are updated by augmenting templates that indicate the behavior of the emitters and training the ML models using many plausible superpositions of the augmented templates. The ML models are updated by selecting and applying non-linear augmentations of at least one of the templates and new templates randomly using a Monte Carlo approach.