Mismatched filtering is a well-known approach of range sidelobe suppression in radar pulse compression; however, advances in radar waveform agility and spectrum sharing require the generation of filters to be efficiently computed (for possible real-time operation) with the consideration of possible interference via collisions with other spectral users. Here, a computationally efficient radar mismatched filter (MMF) design framework is proposed, which provides the optimal integrated sidelobe level (ISL) or spectral template match for a constrained signal-to-interference-plus-noise ratio loss via the Lagrange dual problem with known interference. This approach builds off of previous works, which examine ISL MMFs with constrained signal-to-noise ratio loss and provides further extensions, analysis, and bounds for alternative quadratic constraints and design objectives. Furthermore, by leveraging the Toeplitz structure that arises in time-series problems and the bounds of the Lagrange dual function, efficient solvers are developed that leverage infinite-impulse-response-based circulant approximations and/or preconditioned conjugate gradient. The proposed filter design is compared against current linear solvers and is found to have a lower order computational complexity with less required sequential calculations. Filter performance is assessed via hardware-in-the-loop and open-air experimental measurements.


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    Titel :

    Computationally Efficient Constraint-Optimized Radar Mismatched Filtering for Waveform-Agile Systems


    Beteiligte:


    Erscheinungsdatum :

    01.06.2025


    Format / Umfang :

    7884415 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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