A deep neural network (DNN) is used for achieving subpulse resolution in noncoherent stepped frequency waveform radar. The tradeoff between high resolution and long range in radar systems is often addressed using pulse compression, allowing both long pulses and high resolution by increasing the pulse bandwidth. This typically requires a coherent radar. In this article we present a deep learning-based solution for achieving subpulse resolution with a noncoherent radar. Our results for such a system are comparable to an equivalent coherent system for signal-to-noise ratios (SNRs) greater than 10 dB. All results are based on simulated data.


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    Title :

    Stepped Frequency Pulse Compression With Noncoherent Radar Using Deep Learning


    Contributors:


    Publication date :

    2021-06-01


    Size :

    4524942 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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