System and method for learned emitter identification and tracking

A machine learning-based system addresses the limitations of static MDFs by adaptively identifying and tracking radar emitters, improving situational awareness and mission success in dynamic combat environments.

WO2026010990A1 Publication Date: 2026-01-08RAYTHEON CO
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Patent Information

Application Number
PCT/US2025/036136
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-07-02
Filing Date
2025-07-01
Publication Date
2026-01-08

AI Technical Summary

Technical Problem

Traditional radar emitter identification systems rely on static Mission Data Files (MDFs) that struggle with agile radar systems, unknown emitters, and high computational demands, leading to inefficiencies and reduced effectiveness in dynamic combat environments.

Method used

A machine learning-based system that integrates supervised and unsupervised algorithms to analyze pulse descriptor words, enabling adaptive identification and tracking of both known and unknown radar emitters, reducing dependency on extensive databases and improving processing efficiency.

Benefits of technology

Enhances situational awareness and mission success by accurately identifying and tracking radar emitters, maintaining adaptability to new threats, and reducing computational burden in real-time scenarios.

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Abstract

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.
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