Identification friend or foe, or its civilian variant known as secondary surveillance radar, is a cooperative airborne identification system that uses coded pulses for providing identity information via an interrogation–response mechanism. The major challenge in this mechanism is the difficulty in correctly decoding the IFF mode code data from the received responses, especially at low signal-to-noise ratios. Due to the high tolerances allowable (as per the Standardization Agreement 4193 and International Civil Aviation Organization Annex 10 aeronautical telecommunication standards for pulse width, pulse positions, and frame width), the statistical variations in the replies are high, which makes the rule-based algorithms complex and less robust. Also, the decoding becomes increasingly difficult due to the garbling of pulses from multiple close-by transponders. To address these challenges of IFF reply processing, this paper proposes modular blocks based on a deep neural network for end-to-end reply processing in the IFF system. IFF pulse detection and degarbling are performed using a convolutional neural-network-based approach, followed by code detection using a recurrent neural-network-based approach, and code tracking using a fully connected network. Because the network is trained to learn the statistical variations in data, it outperforms the conventional rule-based signal processing in terms of probabilistic code detection.


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    Titel :

    Identification Friend or Foe Mode Code Detection Using Deep Pulse Detector Network


    Beteiligte:
    Dhipu, T. M. (Autor:in) / Rishabh, Verma (Autor:in) / Rajesh, R. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-10-31


    Format / Umfang :

    8 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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