Radar Emitter Identification Using ML Deinterleaving and Tracking
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Solution Overview
Problem
Traditional radar emitter identification systems relying on Mission Data Files (MDFs) struggle with the agility of modern radar systems, the handling of unknown emitters, and computational demands in limited processing environments, leading to ineffective response strategies in dynamic combat scenarios.
Innovation Solution
A system that integrates machine learning techniques to dynamically classify and track radar emitters, using a combination of supervised and unsupervised algorithms to analyze pulse descriptor words, reducing dependency on static databases and enhancing adaptability to new threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional Mission Data Files (MDFs) are used for radar emitter identification, then the system can identify known emitters using predefined databases, but the system fails to effectively handle unknown emitters and agile modern radar systems
Solution Approach 1:
The system transitions from static MDF databases to dynamic machine learning models that continuously learn and adapt to new emitter patterns in real-time, enabling the system to handle both known and unknown emitters effectively
Solution Approach 2:
The machine learning model automatically updates and refines its classification capabilities through continuous learning from incoming signal data, eliminating the need for manual database updates and maintaining effectiveness against evolving threats
2Adaptability or versatility
If comprehensive Mission Data Files are maintained to cover all possible emitters, then the identification coverage is improved, but the computational burden increases beyond the processing capacity of the EW environment
Solution Approach 1:
The system extracts only the most relevant features from radar signals (pulse width, repetition interval, frequency, amplitude) to feed into the machine learning model, reducing computational complexity while maintaining identification accuracy
Solution Approach 2:
The system transforms the identification approach from database matching based on multiple parameters to machine learning classification based on key signal characteristics, reducing computational burden while improving adaptability
3Device complexity
If static databases are used for emitter identification, then the system structure is simple, but the system cannot keep up with the agility of modern radar systems that change operational parameters
Solution Approach 1:
The system replaces static databases with dynamic machine learning models that continuously adapt to changing radar behaviors, maintaining simplicity in structure while achieving high adaptability through automated learning
Data Source
AI summary
A system and method are described for emitter identification and tracking in an electronic warfare (EW) environment. The system includes an antenna array configured to receive signals from radio frequency (RF) emitters during a dwell. Processing circuitry converts the received signals into digital signals. Pulses are detected and characteristics of the pulses determined to form pulse descriptor words (PDWs). The PDWs obtained during the dwell are deinterleaved using unsupervised machine learning to form clusters. The clusters are categorized using one or more supervised machine learning algorithms to determine whether the PDWs correspond to known or unknown emitters and the results tracked as in or out of library emitters. After merging the in or out of library emitters, an emitter report is generated and used to update a library of emitter profiles used by the supervised machine learning algorithms as well as determine countermeasures to generate.