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Activity title

Electronic Support (ES) Techniques Enabling Cognitive Electronic Warfare (EW)

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Systems Concepts and Integration

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Cognitive EW, Cognitive RF, Electronic Support, Electronic Warfare, Machine Learning, Spectrum Operations


Emerging radars and radios are capable of changing their transmitted waveforms by modifying their modulation, bandwidth, center frequency, medium access protocols and timing. The ability of radars and radios to change their waveforms and medium access protocols challenges traditional signal detection and identification techniques. These traditional techniques compare detected signal characteristics with known signal parameters in a pre-defined database. Further complicating the picture is the possibility that distributed threats may cooperate with one another for improved situational awareness. To maintain the capabilities of signal interception and interpretation, new identification and classification methods are needed. Traditionally these methods are closely related to the signal data bases owned by the NATO nations, within which data exchange formats are defined at the NATO level. New identification and classification methods will result in additional features and parameters extracted from the signals which will not be covered by the current data formats. Additionally, future classification and identification techniques will require the ability this expanded signal parameter set facilitates to identify detected signal ‘intent’ in the absence of a priori awareness of the signal, as well as which threat systems are cooperating with one another.


This effort will assess combined communication and radar signal detection, classification and network topology identification using machine intelligence (MI) approaches in NATO relevant scenarios. In support of this assessment, this NATO activity will identify the challenges of applying intelligent agents to tactical NATO ES operations, explore options for accessing relevant data for training intelligent agents, analyze the utility of various machine intelligent approaches in NATO ES, identify requirements and limitations of hardware and NATO platforms to support machine intelligence and identify the requirements for data and models to support training of intelligent agents.


• Applicability of intelligent agents to NATO-relevant scenarios for robust detection, classification and network topology identification • Reliable threat determination from spectrum observations • Current and future limitations of intelligent agents for ES • Hardware and software processing requirements for detection, classification, topology identification and threat assessment • NATO relevant training data required to support robust MI operation

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