Dwell and switch (D&S) pulse repetition intervals (PRIs) are vital in modern radar systems. However, the existing deinterleaving methods that do not rely on prior information are generally ineffective at handling D&S signals. Considering the characteristics of D&S PRIs, spectrum analysis and maximal clique are employed in this study to deinterleave D&S signals in a prior unknown scenario. Sequence search technology is introduced for D&S subsequences to ensure complete D&S subsequences within the search results. Furthermore, D&S subsequence identification technology based on spectrum analysis is developed. Following vector processing of the search results, D&S subsequences are distinguished from fixed sequences based on the number of spectral peaks within the principal interval. In addition, a subsequence consolidation technique based on the maximal clique is introduced, where the sequence search result is regarded as the vertex, and the existence of an edge between corresponding vertices is determined by evaluating the complementarity and mutual exclusion of the combination. Through the construction of an undirected graph, subsequences from the same D&S sequence are consolidated using the maximal clique extraction. The simulation results demonstrate that the detection threshold can effectively identify subsequences, whereas the complementarity and mutual exclusion constraints efficiently detect associations between subsequences, mitigating false association pairs from false alarms of fixed sequences. For a pulse missing rate of 30% or less, the analysis accuracy of D&S parameters reaches 98.23%, highlighting the outstanding performance and promising application prospects of the proposed method in deinterleaving D&S sequences and decoding their modulation patterns.


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    Title :

    Deinterleaving Method for Radar Signals With Dwell and Switch Pulse Repetition Intervals in a Prior Unknown Scenario


    Contributors:
    Xie, Min (author) / Zhao, Chuang (author) / Huang, Jie (author) / Sun, Yi-Fan (author) / Hu, De-Xiu (author) / Wang, Ze-Wen (author)


    Publication date :

    2024-12-01


    Size :

    2664065 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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