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.

中文翻译：

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使用深度学习的非相干雷达步进频率脉冲压缩

深度神经网络 (DNN) 用于在非相干步进频率波形雷达中实现亚脉冲分辨率。雷达系统中高分辨率和长距离之间的权衡通常使用脉冲压缩来解决，通过增加脉冲带宽允许长脉冲和高分辨率。这通常需要相干雷达。在本文中，我们提出了一种基于深度学习的解决方案，用于通过非相干雷达实现亚脉冲分辨率。我们对此类系统的结果可与信噪比 (SNR) 大于 10 dB 的等效相干系统相媲美。所有结果均基于模拟数据。