Dec 22, 2020 · A deep neural network (DNN) is used for achieving subpulse resolution in noncoherent stepped frequency waveform radar.

This typically requires a coherent radar. In this study we present a deep learning based solution for achieving subpulse resolution with a non-coherent radar.

Apr 15, 2021 · A deep neural network (DNN) is used for achieving subpulse resolution in non-coherent stepped frequency waveform radar. The trade-off between ...

A deep learning-based solution for achieving subpulse resolution with a noncoherent radar that is comparable to an equivalent coherent system for ...

In this study we use a non-coherent stepped frequency waveform and a deep neural network (DNN) acting as a pulse compression “filter.” Unlike SNR gain, a ...

This typically requires a coherent radar. In this study we present a deep learning based solution for achieving subpulse resolution with a non-coherent radar.

(DOI: 10.1109/TAES.2020.3046336) A deep neural network (DNN) is used for achieving subpulse resolution in noncoherent stepped frequency waveform radar.

A new method that uses neural-network-based deep learning could lead to faster and more accurate holographic image reconstruction and phase recovery.

This paper proposes a novel technique for implementation of NCPC that is based on well-known m-sequences along with a modified coding scheme.

Stepped Frequency Pulse Compression With Noncoherent Radar Using Deep Learning. IEEE Transactions on Aerospace and Electronic Systems, 2021. BookmarkDownload ...